How to load locally saved tensorflow DistillBERT model
I have got tf model  for DistillBERT by the following python line

> `import tensorflow as tf
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"), dtype="int32")[None, :]  # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0]`

> These lines have been executed successfully. But I am facing error with model.save()

> `model.save("DSB/DistilBERT.h5")` 
> `model.save("DSB")`
> `model.save("DSB/")`
> all the above 3 line gives errors
![Screenshot (288)](https://user-images.githubusercontent.com/48640299/73131673-852d3c00-4035-11ea-88fc-2641887bb951.png)
![Screenshot (289)](https://user-images.githubusercontent.com/48640299/73131716-46e44c80-4036-11ea-8a12-3ec00b309341.png)

> but downlines works
>`model.save_pretrained("DSB")`
this saves 2 file tf_model.h5 and config.json 
>`model.save_weights("DSB/DistDistilBERT_weights.h5")` 
this  also have saved the file
![Screenshot (290)](https://user-images.githubusercontent.com/48640299/73131807-b3138000-4037-11ea-91ac-d2f4106e24d4.png)
> but I am not able to re-load this locally saved model any how, I have tried with all down-lines it gives error

> `from tensorflow.keras.models import load_model
from transformers import DistilBertConfig, PretrainedConfig
from transformers import TFPreTrainedModel
config = DistilBertConfig.from_json_file('DSB/config.json') conf2=PretrainedConfig.from_pretrained("DSB")
config=TFPreTrainedModel.from_config("DSB/config.json")`
> all these load configuration , but I am unable to load model , tried with all down-line
> `model=TFPreTrainedModel.from_pretrained("DSB")`

> `model=PreTrainedModel.from_pretrained("DSB/tf_model.h5", from_tf=True, config=config)`

> `model=TFPreTrainedModel.from_pretrained("DSB/")`

> ` model=TFPreTrainedModel.from_pretrained("DSB/tf_model.h5", config=config)`


![Screenshot (291)](https://user-images.githubusercontent.com/48640299/73131818-d3433f00-4037-11ea-910f-9265008b3d77.png)

> NotImplementedError                       Traceback (most recent call last)
<ipython-input-28-7f562f1af321> in <module>()
      1 from transformers import TFPreTrainedModel
----> 2 model=TFPreTrainedModel.from_pretrained("DSB/tf_model.h5", config=config)
      3 #config=TFPreTrainedModel.from_config("DSB/config.json")
      4 #model=TFPreTrainedModel.from_pretrained("DSB/")

2 frames
/usr/local/lib/python3.6/dist-packages/transformers/modeling_tf_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    309             return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
    310 
--> 311         ret = model(model.dummy_inputs, training=False)  # build the network with dummy inputs
    312 
    313         assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    820           with base_layer_utils.autocast_context_manager(
    821               self._compute_dtype):
--> 822             outputs = self.call(cast_inputs, *args, **kwargs)
    823           self._handle_activity_regularization(inputs, outputs)
    824           self._set_mask_metadata(inputs, outputs, input_masks)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in call(self, inputs, training, mask)
    710     """
    711     if not self._is_graph_network:
--> 712       raise NotImplementedError('When subclassing the `Model` class, you should'
    713                                 ' implement a `call` method.')
    714 

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method.

Please format your code correctly using code tags and not quote tags, and don't use screenshots but post your actual code so that we can copy-paste it and reproduce your errors. https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks<|||||>Thanks to your response,  now it will be convenient to copy-paste.

```
import tensorflow as tf
from transformers import DistilBertTokenizer, TFDistilBertModel
tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
model = TFDistilBertModel.from_pretrained('distilbert-base-uncased')
input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"), dtype="int32")[None, :]  # Batch size 1
outputs = model(input_ids)
last_hidden_states = outputs[0]
```
>############################################ success 
```
model.save("DSB/SV/distDistilBERT.h5")
```
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-5-c1f33594ba67> in <module>()
----> 1 model.save("DSB/SV/distDistilBERT.h5")

1 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
   1006     """
   1007     save.save_model(self, filepath, overwrite, include_optimizer, save_format,
-> 1008                     signatures, options)
   1009 
   1010   def save_weights(self, filepath, overwrite=True, save_format=None):

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    103         not isinstance(model, sequential.Sequential)):
    104       raise NotImplementedError(
--> 105           'Saving the model to HDF5 format requires the model to be a '
    106           'Functional model or a Sequential model. It does not work for '
    107           'subclassed models, because such models are defined via the body of '

NotImplementedError: Saving the model to HDF5 format requires the model to be a Functional model or a Sequential model. It does not work for subclassed models, because such models are defined via the body of a Python method, which isn't safely serializable. Consider saving to the Tensorflow SavedModel format (by setting save_format="tf") or using `save_weights`.

> #############################################
```
model.save("DSB/")

```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-75503fb9f2ea> in <module>()
----> 1 model.save("DSB/")

3 frames
/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in save(self, filepath, overwrite, include_optimizer, save_format, signatures, options)
   1006     """
   1007     save.save_model(self, filepath, overwrite, include_optimizer, save_format,
-> 1008                     signatures, options)
   1009 
   1010   def save_weights(self, filepath, overwrite=True, save_format=None):

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/saving/save.py in save_model(model, filepath, overwrite, include_optimizer, save_format, signatures, options)
    113   else:
    114     saved_model_save.save(model, filepath, overwrite, include_optimizer,
--> 115                           signatures, options)
    116 
    117 

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/saving/saved_model/save.py in save(model, filepath, overwrite, include_optimizer, signatures, options)
     63 
     64   if save_impl.should_skip_serialization(model):
---> 65     saving_utils.raise_model_input_error(model)
     66 
     67   if not include_optimizer:

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/saving/saving_utils.py in raise_model_input_error(model)
    111       'set. Usually, input shapes are automatically determined from calling'
    112       ' .fit() or .predict(). To manually set the shapes, call '
--> 113       'model._set_inputs(inputs).'.format(model))
    114 
    115 

ValueError: Model <transformers.modeling_tf_distilbert.TFDistilBertModel object at 0x7f6905c1fbe0> cannot be saved because the input shapes have not been set. Usually, input shapes are automatically determined from calling .fit() or .predict(). To manually set the shapes, call model._set_inputs(inputs).

>#######################################################
```
model.save_pretrained("DSB")
model.save_weights("DSB/DistDistilBERT_weights.h5")
```
>######################################################### success 
```
from transformers import DistilBertConfig, PretrainedConfig
config = DistilBertConfig.from_json_file('DSB/config.json')
conf2=PretrainedConfig.from_pretrained("DSB")

```
> ############################################################# success 
```
#from tensorflow.keras.models import load_model
#model=load_model("DSB/tf_model.h5") # error 

```
> ################ error, It looks because-of saved model is not by `model.save("path")`
```
from transformers import TFPreTrainedModel
#model=TFPreTrainedModel.from_pretrained("DSB") # error 
model=TFPreTrainedModel.from_pretrained("DSB/tf_model.h5", config=config) # error 
#config=TFPreTrainedModel.from_config("DSB/config.json") # error 
#model=TFPreTrainedModel.from_pretrained("DSB/") # error 
```
---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
<ipython-input-28-7f562f1af321> in <module>()
      1 from transformers import TFPreTrainedModel
       2 #model=TFPreTrainedModel.from_pretrained("DSB") # error 
----> 3 model=TFPreTrainedModel.from_pretrained("DSB/tf_model.h5", config=config)
      4 #config=TFPreTrainedModel.from_config("DSB/config.json")
      5 #model=TFPreTrainedModel.from_pretrained("DSB/")

2 frames
/usr/local/lib/python3.6/dist-packages/transformers/modeling_tf_utils.py in from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
    309             return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)
    310 
--> 311         ret = model(model.dummy_inputs, training=False)  # build the network with dummy inputs
    312 
    313         assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py in __call__(self, inputs, *args, **kwargs)
    820           with base_layer_utils.autocast_context_manager(
    821               self._compute_dtype):
--> 822             outputs = self.call(cast_inputs, *args, **kwargs)
    823           self._handle_activity_regularization(inputs, outputs)
    824           self._set_mask_metadata(inputs, outputs, input_masks)

/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py in call(self, inputs, training, mask)
    710     """
    711     if not self._is_graph_network:
--> 712       raise NotImplementedError('When subclassing the `Model` class, you should'
    713                                 ' implement a `call` method.')
    714 

NotImplementedError: When subclassing the `Model` class, you should implement a `call` method.
<|||||>To save/load a model:

```py

model = TFDistilBertModel(config)

# Saving the model
model.save_pretrained("directory")

# Loading the model
loaded_model = TFDistilBertModel.from_pretrained("directory")  # automatically loads the configuration.
```<|||||>Thanks @LysandreJik 
It works.
greedy guidelines poped by `model.svae_pretrained` have confused me. It pops up like this 
```
model.save_pretrained("directory")

save a model and its configuration file to the directory, so that it can be re-loaded using the 
:func: ~transformers.PreTrainedModel.from_pretrained` 
class method

```

![Screenshot (296)](https://user-images.githubusercontent.com/48640299/73247454-df193780-41d6-11ea-92de-1372d11291b2.png)
