BertEncoder has no attribute 'bias' when convert tf checkpoint
# ❓ Questions & Help
BertEncoder object has no attribute 'bias' when convert tf checkpoint
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## Details
<!-- Description of your issue -->
I tried to convert my pretrained BERT tf checkpoint but I got this error:
```
/usr/local/lib/python3.6/dist-packages/transformers/modeling_bert.py in load_tf_weights_in_bert(model, config, tf_checkpoint_path)
    133                 pointer = getattr(pointer, "weight")
    134             elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
--> 135                 pointer = getattr(pointer, "bias")
    136             elif scope_names[0] == "output_weights":
    137                 pointer = getattr(pointer, "weight")
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __getattr__(self, name)
    770                 return modules[name]
    771         raise ModuleAttributeError("'{}' object has no attribute '{}'".format(
--> 772             type(self).__name__, name))
    773 
    774     def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:
ModuleAttributeError: 'BertEncoder' object has no attribute 'bias'
```


I used both load_tf_weights_in_bert function and BertForPreTraining but it was not working.

My code:
```
config = BertConfig.from_json_file('bertvn_base/bertvn_base_config.json')
model = BertForPreTraining.from_pretrained('bertvn_base/model.ckpt', from_tf=True, config=config)
```

My config:
```
{"attention_probs_dropout_prob": 0,
"hidden_act": "gelu",
  "hidden_dropout_prob": 0,
  "embedding_size": 768,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "max_position_embeddings": 192,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "num_hidden_groups": 12,
  "net_structure_type": 0,
  "gap_size": 0,
  "num_memory_blocks": 0,
  "inner_group_num": 1,
  "down_scale_factor": 1,
  "type_vocab_size": 2,
  "vocab_size": 120000
}
```

Thanks for your help!
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