Too many bugs in Version 2.5.0
1. It cannot be installed on MacOS. By runing `pip install -U transformers`, I got the following errors: 
>   Building wheels for collected packages: tokenizers
>   Building wheel for tokenizers (PEP 517) ... error
>   ERROR: Command errored out with exit status 1:
>   command: /anaconda/bin/python /anaconda/lib/python3.7/site-packages/pip/_vendor/pep517/_in_process.py build_wheel /var/folders/5h/fr2vhgsx4jd8wz4bphzt22_8p1v0bf/T/tmpfh6km7na
>   cwd: /private/var/folders/5h/fr2vhgsx4jd8wz4bphzt22_8p1v0bf/T/pip-install-fog09t3h/tokenizers
>   Complete output (36 lines):
>   running bdist_wheel
>   running build
>   running build_py
>   creating build
>   creating build/lib
>   creating build/lib/tokenizers
>   copying tokenizers/__init__.py -> build/lib/tokenizers
>   creating build/lib/tokenizers/models
>   copying tokenizers/models/__init__.py -> build/lib/tokenizers/models
>   creating build/lib/tokenizers/decoders
>   copying tokenizers/decoders/__init__.py -> build/lib/tokenizers/decoders
>   creating build/lib/tokenizers/normalizers
>   copying tokenizers/normalizers/__init__.py -> build/lib/tokenizers/normalizers
>   creating build/lib/tokenizers/pre_tokenizers
>   copying tokenizers/pre_tokenizers/__init__.py -> build/lib/tokenizers/pre_tokenizers
>   creating build/lib/tokenizers/processors
>   copying tokenizers/processors/__init__.py -> build/lib/tokenizers/processors
>   creating build/lib/tokenizers/trainers
>   copying tokenizers/trainers/__init__.py -> build/lib/tokenizers/trainers
>   creating build/lib/tokenizers/implementations
>   copying tokenizers/implementations/byte_level_bpe.py -> build/lib/tokenizers/implementations
>   copying tokenizers/implementations/sentencepiece_bpe.py -> build/lib/tokenizers/implementations
>   copying tokenizers/implementations/base_tokenizer.py -> build/lib/tokenizers/implementations
>   copying tokenizers/implementations/__init__.py -> build/lib/tokenizers/implementations
>   copying tokenizers/implementations/char_level_bpe.py -> build/lib/tokenizers/implementations
>   copying tokenizers/implementations/bert_wordpiece.py -> build/lib/tokenizers/implementations
>   copying tokenizers/__init__.pyi -> build/lib/tokenizers
>   copying tokenizers/models/__init__.pyi -> build/lib/tokenizers/models
>   copying tokenizers/decoders/__init__.pyi -> build/lib/tokenizers/decoders
>   copying tokenizers/normalizers/__init__.pyi -> build/lib/tokenizers/normalizers
>   copying tokenizers/pre_tokenizers/__init__.pyi -> build/lib/tokenizers/pre_tokenizers
>   copying tokenizers/processors/__init__.pyi -> build/lib/tokenizers/processors
>   copying tokenizers/trainers/__init__.pyi -> build/lib/tokenizers/trainers
>   running build_ext
>   running build_rust
>   error: Can not find Rust compiler
>   ----------------------------------------
>   ERROR: Failed building wheel for tokenizers
>   Running setup.py clean for tokenizers
> Failed to build tokenizers
> ERROR: Could not build wheels for tokenizers which use PEP 517 and cannot be installed directly

2. On Linux, it can be installed, but failed with the following code:
> import transformers
> transformers.AutoTokenizer.from_pretrained("bert-base-cased").save_pretrained("./")
> transformers.AutoModel.from_pretrained("bert-base-cased").save_pretrained("./")
> transformers.AutoTokenizer.from_pretrained("./")
> transformers.AutoModel.from_pretrained("./")

Actually, it is the second line that generates the following errors: 
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "/anaconda/lib/python3.7/site-packages/transformers/tokenization_utils.py", line 587, in save_pretrained
>     return vocab_files + (special_tokens_map_file, added_tokens_file)
> TypeError: unsupported operand type(s) for +: 'NoneType' and 'tuple'

3. The vocabulary size of xlm-roberta is wrong, so it failed with the following code,  (this bug also exist in Version 2.4.1): 
> import transformers
> tokenizer = transformers.AutoTokenizer.from_pretrained("xlm-roberta-base")
> tokenizer.convert_ids_to_tokens(range(tokenizer.vocab_size))

The error is actually caused by the wrong vocab size:
> [libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1506] CHECK failed: (index) < (current_size_): 
> terminate called after throwing an instance of 'google::protobuf::FatalException'
>   what():  CHECK failed: (index) < (current_size_): 
> zsh: abort      python
Hi! Indeed, there have been a few issues as this was the first release incorporating `tokenizers` by default. A new version of `tokenizers` and `transformers` will be available either today or tomorrow and should fix most of these.<|||||>For future reference, when you say that some code "fails", please also provide the stack trace. This helps greatly when debugging.<|||||>Thanks, stack trace provided...
I just noticed that in Version 2.5.0, `AutoTokenizer.from_pretrained()` takes a new argument `use_fast`, and defaults it to `True`. This seems to be the reason for the error, because when I set it to `False`, the loaded model can be correctly saved by `save_pretrained()`. 
I wonder why this `use_fast` argument is added, and why it is default to `True`? <|||||>`use_fast` uses the `tokenizers` library which is a new, extremely fast implementation of different tokenizers. I agree that for the first few releases it might've been better to expose the argument but setting it to False by default as to catch errors only by early adopters. Now many errors are reported that could've otherwise been avoided. In the meantime, you can explicitly set it to False.<|||||>For Tokenizers library: 
1, Where is the document about how to install and use it? The Readme is too brief...
2, I understand that it is designed as a combination of various tokenizers. But to use a pre-trained model, is it better to use the original tokenizer to avoid subtle differences like special tokens? If so, the Transformers library should not use the tokenizers from Tokenizers library by default...<|||||>`tokenizers` sits in its own repository. You can find it [here](https://github.com/huggingface/tokenizers) and its [Python](https://github.com/huggingface/tokenizers/tree/master/bindings/python) bindings here.

I think that the fast tokenizers are tested to get the exact same output as the other ones.<|||||>Thanks...
It seems that `tokenizers` has been installed together with `transformers` by `pip install transformers`? 
In the future, will the tokenizer classes (e.g. BertTokenizer, AutoTokenizer, etc.) still be kept in the `transformers` library? Or they will be deprecated? <|||||>I cannot answer that, I don't know what the roadmap looks like.<|||||>Install Python 64-bit instead of 32-bit solved my same issue.<|||||>Which issue did you solved? 
I think 64-bit Python is almost used by everyone...<|||||>1) This issue should be opened on [huggingface/tokenizers](https://github.com/huggingface/tokenizers) as it is an installation issue with the `huggingface/tokenizers` library.

2) This issue is solved in the current master (and 2.5.1) as well.

3) This is fixed in https://github.com/huggingface/transformers/pull/3198 which will be merged in a bit.<|||||>i still have this prob, is anyone can tell me how to solve it?<|||||>which problem?<|||||>Still seeing the error 
```
[libprotobuf FATAL /sentencepiece/src/../third_party/protobuf-lite/google/protobuf/repeated_field.h:1506] CHECK failed: (index) < (current_size_): 
terminate called after throwing an instance of 'google::protobuf::FatalException'
  what():  CHECK failed: (index) < (current_size_): 
```
how do I work around this?<|||||>Hi @catyeo18, please provide the code that gets you this error, alongside the different versions of software you're using. Here's the [template](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title=) for bug reports. Thank you.