if cutoffs=[], convert_transfo_xl_checkpoint_to_pytorch.py has a bug
## 🐛 Bug

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Model I am using (Bert, XLNet....): transformer_xl

Language I am using the model on (English, Chinese....): I train xl model base on own dataset

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## Environment

* OS: centos 7.3
* Python version: python3.6
* PyTorch version: torch1.1
* PyTorch Transformers version (or branch): 1.0.0
* Using GPU: yes
* Distributed of parallel setup ? no
* Any other relevant information: no 

## detail context

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AttributeError: 'ProjectedAdaptiveLogSoftmax' object has no attribute  'cluster_weight'

I see 'ProjectedAdaptiveLogSoftmax' code, if len(cutoffs) - 1 > 0,  will have attribute  'cluster_weight'

Hi!

Yes, you should indeed specify `cutoffs` in your `TransfoXLConfig` or the adaptive softmax won't be able to create its clusters. We should probably put a more explicit error.<|||||>Hello, @LysandreJik

The checkpoint of https://github.com/kimiyoung/transformer-xl doesn't have cutoff_N when adaptive softmax is not used. Does PyTorch-Transformers support TransfoXLConfig.adaptive = False? If supported, should it read checkpoint without explicit error? The content of checkpoint is like this without adaptive softmax.

```
transformer/adaptive_embed/lookup_table (DT_FLOAT) [32768,512]
transformer/adaptive_embed/lookup_table/Adam (DT_FLOAT) [32768,512]
transformer/adaptive_embed/lookup_table/Adam_1 (DT_FLOAT) [32768,512]
transformer/adaptive_softmax/bias (DT_FLOAT) [32768]
transformer/adaptive_softmax/bias/Adam (DT_FLOAT) [32768]
transformer/adaptive_softmax/bias/Adam_1 (DT_FLOAT) [32768]
transformer/layer_0/ff/LayerNorm/beta (DT_FLOAT) [512]
```<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
