TFEncoderDecoder not handling labels correctly
## Environment info
Google Colab

- `transformers` version: master branch. With the latest release (4.12.3) you can't replicate this problem, as it fails with other issue that has already been fixed in master (support for cross-attention in TF GPT2)
- Tensorflow version: 2.7.0

## Who can help

Tagging @patrickvonplaten as he has done the latest merges on TFEncoderDecoder.

## Information

In TFEncoderDecoder, when the input is passed as dict, the encoder `input_processing` function "unpacks it", also unpacking the labels (if they are there). The labels end up being passed to the encoder call, which shouldn't happen, as the labels are only needed for the decoder, and causes the encoder call to fail.

The consequence is that trying to fit a TFEncoderDecoder using `.fit()` with a tf.data.Dataset results in this error.

## To reproduce

```
from transformers import TFEncoderDecoderModel
model = TFEncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "gpt2")
model(model.dummy_inputs)  # works fine

with_labels = dict(labels=model.dummy_inputs["decoder_input_ids"], **model.dummy_inputs)
model(**with_labels)  # works fine
model(with_labels)  # fails with the error bellow
```

```
/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_tf_encoder_decoder.py in call(self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, encoder_outputs, past_key_values, inputs_embeds, decoder_inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
    493                 decoder_attention_mask = encoder_inputs.pop("decoder_attention_mask")
    494 
--> 495             encoder_outputs = self.encoder(**encoder_inputs)
    496 
    497         encoder_hidden_states = encoder_outputs[0]

/usr/local/lib/python3.7/dist-packages/transformers/models/bert/modeling_tf_bert.py in call(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict, training, **kwargs)
   1125             return_dict=return_dict,
   1126             training=training,
-> 1127             kwargs_call=kwargs,
   1128         )
   1129         outputs = self.bert(

/usr/local/lib/python3.7/dist-packages/transformers/modeling_tf_utils.py in input_processing(func, config, input_ids, **kwargs)
    386     if len(kwargs["kwargs_call"]) > 0:
    387         raise ValueError(
--> 388             f"The following keyword arguments are not supported by this model: {list(kwargs['kwargs_call'].keys())}."
    389         )
    390 

ValueError: Exception encountered when calling layer "encoder" (type TFBertModel).

The following keyword arguments are not supported by this model: ['labels'].

Call arguments received:
  • input_ids=tf.Tensor(shape=(3, 5), dtype=int32)
  • attention_mask=None
  • token_type_ids=None
  • position_ids=None
  • head_mask=None
  • inputs_embeds=None
  • encoder_hidden_states=None
  • encoder_attention_mask=None
  • past_key_values=None
  • use_cache=True
  • output_attentions=False
  • output_hidden_states=False
  • return_dict=True
  • training=False
  • kwargs={'labels': 'tf.Tensor(shape=(3, 5), dtype=int32)'}
```


## Expected behavior

This should handle labels correctly, as they are needed in order to fit the model.

A workaround that works is adding this bit on the call:

```
encoder_inputs = input_processing(**encoder_processing_inputs)
# start new code
if "labels" in encoder_inputs:
    labels = encoder_inputs.pop("labels")
# end new code
...
```
Hi,

I think all the inputs should be unpacked as keyword arguments before inputted into `TFEncoderDecoderModel.__call__`, 
as stated in the [docs](https://huggingface.co/transformers/model_doc/encoderdecoder.html#tfencoderdecodermodel), 

Is there any reason that you want to pass a dict directly?<|||||>The inputs are not unpacked in the model train_step(), which is what is used when you train the model using fit().

See TFPretrainedModel.train_step (line 802):

```
        with tf.GradientTape() as tape:
            y_pred = self(x, training=True)
```

<|||||>@Rocketknight1,

Do you maybe find some time to look into this? :-)<|||||>@Rocketknight1 , @NielsRogge @ydshieh - I think we can solve this issue with the new design now no?<|||||>I didn't follow this issue until now. I can try to look at this if @Rocketknight1 is OK.<|||||>@ydshieh Sure, yes! I'm sorry I've been slow with it.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>activate :-)