add openwebtext
BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually. > BTW, is there a one-line command to make our building scripts pass flake8 test? (included code quality test), I got like trailing space or mixed space and tab warning and error, and fixed them manually.

I don't think so.
We have a command for black and isort but not flake8 as far as I know. Thanks for your awesome work too.
BTW a little reminder, this solves #132 
This adds [The OpenWebText Corpus](https://skylion007.github.io/OpenWebTextCorpus/), which is a clean and large text corpus for nlp pretraining. It is an open source effort to reproduce OpenAI’s WebText dataset used by GPT-2, and it is also needed to reproduce ELECTRA.

It solves #132 .

### Besides dataset building script, I made some changes to the library.

1. Extract large amount of compressed files with multi processing
I add a `num_proc` argument to `DownloadManager.extract` and pass this `num_proc` to `map_nested`. So I can decompress 20 thousands compressed files faster. `num_proc` I add is default to `None`, so it shouldn't break any other thing.

2. In `cached_path`, I change the order to deal with different kind of compressed files (zip, tar, gzip)
Because there is no way to 100% detect a file is a zip file (see [this](https://stackoverflow.com/questions/18194688/how-can-i-determine-if-a-file-is-a-zip-file)), I found it wrongly detect `'./datasets/downloads/extracted/58764bd6898fa339b25d92e7fbbc3d8dbf64fb504edff1a30a1d7d99d1561027/openwebtext/urlsf_subset13-630_data.xz'` as a zip and try decompress it with zip, sure it will get error. So I made it detect wheter the file is tar or gzip first and detect zip in the last.

3. `MockDownloadManager.extract`
Cuz I pass `num_proc` to `DownloadManager.extract`, I also have to make `MockDownloadManager.extract` to accept extra keywork arguments. So I make it `extract(path, *args, **kwargs)`, but just return the path as original implementation.

**Note**: If there is better way for points mentioned above, thought I would like to help, unless we can solve point4 (make dataset building fast), I may not be able to afford rebuild the dataset again because of change of the dataset script (Building the dataset cost me 4 days). 

### There is something I think we can improve

4. Long time to decompress compressed files
Even I decompress those 20 thousands compressed files with 12 process on my 16 core 3.x Ghz server. It still took about 3 ~ 4days to complete dataset building. Most of time spent on decompress those files.

### Info about the source data
The source data is an  tar.xz file with following structure, files/directory beyond compressed file is what can we get after decompress it.
```
openwebtext.tar.xz
  |__ openwebtext
         |__subset000.xz
         |     |__ ....txt
         |     |__ ....txt
         |     ...
         |__ subset001.xz
         |
         ....
```
And this the structure of dummy data, same as the original one.
```
dummy_data.zip
  |__ dummy_data
         |__ openwebtext
              |__fake_subset-1_data-dirxz # actually it is a directory
              |     |__ ....txt
              |     |__ ....txt
              |__ fake_subset-2_data-dirxz
                    |__ ....txt
                    |__ ....txt
```