Module 'keras.engine.data_adapter' has no attribute 'expand_1d' with non dummy loss
Author: ZJaume
Pull request: no

### System Info

- `transformers` version: 4.25.1
- Platform: Linux-4.15.0-200-generic-x86_64-with-glibc2.17
- Python version: 3.8.13
- Huggingface_hub version: 0.11.1
- PyTorch version (GPU?): 1.10.1+cu102 (True)
- Tensorflow version (GPU?): 2.11.0 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help?

@Rocketknight1 

### Information

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

### Tasks

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

### Reproduction

Run the example code with a non dummy loss:

```python
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
from tensorflow.keras.optimizers import Adam
from datasets import load_dataset
import tensorflow as tf
import numpy as np

dataset = load_dataset("glue", "cola")
dataset = dataset["train"]  # Just take the training split for now


tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
tokenized_data = dict(tokenizer(dataset["sentence"], return_tensors="np", padding=True))

labels = np.array(dataset["label"])  # Label is already an array of 0 and 1

# Load and compile our model
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
# Lower learning rates are often better for fine-tuning transformers
model.compile(optimizer=Adam(3e-5), loss='binary_crossentropy')

model.fit(tokenized_data, labels)
```

```python
Traceback (most recent call last):
  File "test_mirrored.py", line 22, in <module>
    model.fit(tokenized_data, labels)
  File "/home/user/bicleaner-ai-trainings/venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/tmp/__autograph_generated_file1a59fb96.py", line 15, in tf__train_function
    retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
  File "/home/user/bicleaner-ai-trainings/venv/lib/python3.8/site-packages/transformers/modeling_tf_utils.py", line 1476, in train_step
    data = data_adapter.expand_1d(data)
AttributeError: in user code:

    File "/home/user/bicleaner-ai-trainings/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1249, in train_function  *
        return step_function(self, iterator)
    File "/home/user/bicleaner-ai-trainings/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1233, in step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "/home/user/bicleaner-ai-trainings/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1222, in run_step  **
        outputs = model.train_step(data)
    File "/home/user/bicleaner-ai-trainings/venv/lib/python3.8/site-packages/transformers/modeling_tf_utils.py", line 1476, in train_step
        data = data_adapter.expand_1d(data)

    AttributeError: module 'keras.engine.data_adapter' has no attribute 'expand_1d'
```

### Expected behavior

Training succesfully.

Comments:
cc @Rocketknight1 and @gante 

Reproduced this issue locally, seems to be an issue with TF 2.11 and doesn't occur in previous versions. Checking it out now!

@ZJaume @mcclunatic the fix has been merged - please try installing transformers from main with `pip install --upgrade git+https://github.com/huggingface/transformers.git` and see if the issue is resolved. If you encounter any further problems, please reopen this issue and let me know!

@Rocketknight1 I've just tested it in my notebook and the issue is indeed resolved! Thanks so much for fixing this so quickly!

came across this issue experiencing the same thing. upgraded from the primary branch worked for me as well 🚀