Error while saving model: TypeError: ('Not JSON Serializable:', DistilBertConfig
# 🐛 Bug

## Information

In this problem, I am using the pre-trained **distillbert** model embedding to build a custom model (See the code snippet below). Everything works perfectly fine except saving the model (See error below). I am using the latest version of the transformer, which is 3.0.0. I could not even save the same model when using the last version 2.11 (see this issue: [https://github.com/huggingface/transformers/issues/4444](https://github.com/huggingface/transformers/issues/4444)).

I was just wondering if you could help me solve the problem. 

## Code

```
config = DistilBertConfig.from_pretrained( 'distilbert-base-uncased')
config.output_hidden_states = False
distillbert_main = TFDistilBertMainLayer(config = config)

input_word_ids = tf.keras.layers.Input(shape=(8,), dtype = tf.int32, name = "input_word_ids"),
x = distillbert_main(input_word_ids)[0]
x = tf.keras.layers.Lambda(lambda seq: seq[:, 0, :])(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Dropout(0.2)(x)
out = tf.keras.layers.Dense(2)(x)

model = tf.keras.Model(inputs=input_word_ids, outputs=out)
for layer in model.layers[:3]:
    layer.trainable = False
model.summary() # Works fine
model.get_config() # Works fine

model.save('./model.h5') # Does not work and produce error

```
## Error

```
TypeError                                 Traceback (most recent call last)
<ipython-input-32-1fbe6dabead0> in <module>
----> 1 model.save('./model.h5')

/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
   1050     """
   1051     save.save_model(self, filepath, overwrite, include_optimizer, save_format,
-> 1052                     signatures, options)
   1053 
   1054   def save_weights(self, filepath, overwrite=True, save_format=None):

/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    133           'or using `save_weights`.')
    134     hdf5_format.save_model_to_hdf5(
--> 135         model, filepath, overwrite, include_optimizer)
    136   else:
    137     saved_model_save.save(model, filepath, overwrite, include_optimizer,

/usr/local/lib/python3.7/site-packages/tensorflow/python/keras/saving/hdf5_format.py in save_model_to_hdf5(model, filepath, overwrite, include_optimizer)
    111       if isinstance(v, (dict, list, tuple)):
    112         f.attrs[k] = json.dumps(
--> 113             v, default=serialization.get_json_type).encode('utf8')
    114       else:
    115         f.attrs[k] = v

/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/__init__.py in dumps(obj, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
    236         check_circular=check_circular, allow_nan=allow_nan, indent=indent,
    237         separators=separators, default=default, sort_keys=sort_keys,
--> 238         **kw).encode(obj)
    239 
    240 

/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py in encode(self, o)
    197         # exceptions aren't as detailed.  The list call should be roughly
    198         # equivalent to the PySequence_Fast that ''.join() would do.
--> 199         chunks = self.iterencode(o, _one_shot=True)
    200         if not isinstance(chunks, (list, tuple)):
    201             chunks = list(chunks)

/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/json/encoder.py in iterencode(self, o, _one_shot)
    255                 self.key_separator, self.item_separator, self.sort_keys,
    256                 self.skipkeys, _one_shot)
--> 257         return _iterencode(o, 0)
    258 
    259 def _make_iterencode(markers, _default, _encoder, _indent, _floatstr,

/usr/local/lib/python3.7/site-packages/tensorflow/python/util/serialization.py in get_json_type(obj)
     74     return obj.__wrapped__
     75 
---> 76   raise TypeError('Not JSON Serializable:', obj)

TypeError: ('Not JSON Serializable:', DistilBertConfig {
  "activation": "gelu",
  "architectures": [
    "DistilBertForMaskedLM"
  ],
  "attention_dropout": 0.1,
  "dim": 768,
  "dropout": 0.1,
  "hidden_dim": 3072,
  "initializer_range": 0.02,
  "max_position_embeddings": 512,
  "model_type": "distilbert",
  "n_heads": 12,
  "n_layers": 6,
  "pad_token_id": 0,
  "qa_dropout": 0.1,
  "seq_classif_dropout": 0.2,
  "sinusoidal_pos_embds": false,
  "tie_weights_": true,
  "vocab_size": 30522
}
)
```
     
- `transformers` version: 3.0.0
- Platform: Mac OSX
- Python version: 3.7
- PyTorch version (GPU?): No
- Tensorflow version: 2.2.0
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: NO

Hi! The way to save the transformers model is using the `save_pretrained` method, which saves both the configuration and the model as an h5 file. Can you try using it instead?<|||||>> Hi! The way to save the transformers model is using the `save_pretrained` method, which saves both the configuration and the model as an h5 file. Can you try using it instead?

I am not saving the "transformers model" instead use it as a top layer of a Keras model. Then error occurs when saving the model that includes the "transformers model."<|||||>Ok, maybe @jplu or @patrickvonplaten can have a look when they have some bandwidth.<|||||>As a first glance, I can say that it is "normal" because the `DistilBert` model has a config parameter, which doesn't make it compliant with sequential models. Create a subclass model instead to see if it works.

But this is just a quick guess, I will check it deeper when have some time.<|||||>I found that, model could be save in tensorflow saved_model using: 
`tf.saved_model.save(model, './models/model')`

However, I was not able to save in Keras .h5 format. That's fine for me now. So, I close this issue. 