`main_input_name` is None if `predict_with_generate` in keras_callbacks.py for encoder-decoder(Bert-Bert) TF models
Author: saichandrapandraju
Pull request: no

### System Info

- `transformers` version: 4.30.2
- Platform: Linux-5.15.109+-x86_64-with-glibc2.31
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- Safetensors version: 0.3.1
- PyTorch version (GPU?): 2.0.1+cu118 (True)
- Tensorflow version (GPU?): 2.12.0 (True)
- Flax version (CPU?/GPU?/TPU?): 0.7.0 (gpu)
- Jax version: 0.4.13
- JaxLib version: 0.4.13
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@gante , @Rocketknight1 

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)

### Reproduction





```
KeyError                                  Traceback (most recent call last)
[<ipython-input-49-089aabb58b9b>](https://localhost:8080/#) in <cell line: 1>()
----> 1 history = model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=num_epochs, callbacks=callbacks)

1 frames
[/usr/local/lib/python3.10/dist-packages/transformers/keras_callbacks.py](https://localhost:8080/#) in on_epoch_end(self, epoch, logs)
    217             if self.predict_with_generate:
    218                 if isinstance(batch, dict):
--> 219                     generation_inputs = batch[main_input_name]
    220                     attention_mask = batch.get("attention_mask", None)
    221                 else:

KeyError: None
```

[Here's](https://colab.research.google.com/drive/1d75HqymedDSopRDXDBSNIBGT1q7zvCWz?usp=sharing) the colab link to reproduce the error.

because of this code in `keras_callbacks.py` (commented with >>>>> .... <<<<<< for better understanding)- 

```
#### in tf_keras_callback (in func `on_epoch_end`, ~line 191)
main_input_name = None
if self.predict_with_generate:
    # This dense conditional recognizes the case where we have an encoder-decoder model, but
    # avoids getting tangled up when we just have a model with a layer called 'encoder'
    if hasattr(self.model, "encoder") and hasattr(self.model.encoder, "main_input_name"):
        # >>>>>>> If this condition is not satisfied(which is the case currently), `main_input_name` remains None <<<<<<<<
        if self.model.encoder.main_input_name != self.model.main_input_name:
            main_input_name = self.model.encoder.main_input_name
    else:
        main_input_name = getattr(self.model, "main_input_name", "input_ids")

    if self.use_xla_generation and self.generation_function is None:

        def generation_function(inputs, attention_mask):
            return self.model.generate(inputs, attention_mask=attention_mask, **self.generate_kwargs)

        self.generation_function = tf.function(generation_function, jit_compile=True)

prediction_list = []
label_list = []

# The whole predict/generate loop is handled inside this method
for batch in self.eval_dataset:
    if isinstance(batch, tuple):
        batch, labels = batch
    else:
        labels = None
    if self.predict_with_generate:
        if isinstance(batch, dict):
            generation_inputs = batch[main_input_name]  # >>>>>>>>>>>> `main_input_name` remains None here (~line 219) <<<<<<<<<<<<
            attention_mask = batch.get("attention_mask", None)
        else:
            generation_inputs = batch
            attention_mask = None
        if self.use_xla_generation:
            predictions = self.generation_function(generation_inputs, attention_mask=attention_mask)
        else:
            predictions = self.model.generate(
                generation_inputs, attention_mask=attention_mask, **self.generate_kwargs
            )
    
```


### Expected behavior

`main_input_name should` be `input_ids`


for that the following function can be modified - 

```
if hasattr(self.model, "encoder") and hasattr(self.model.encoder, "main_input_name"):
        if self.model.encoder.main_input_name != self.model.main_input_name:
            main_input_name = self.model.encoder.main_input_name
```

to something like - 
```
if hasattr(self.model, "encoder") and hasattr(self.model.encoder, "main_input_name"):
        main_input_name = self.model.encoder.main_input_name
```



Comments:
Thank you for reproting @saichandrapandraju 🤗 

cc @Rocketknight1 (when he is back, or I can take a look after finishing some other tasks ) 

Sure @ydshieh , once this is verified to be valid, I can create a PR 🙂