clean_up_tokenization_spaces=False does not behave correctly in AutoTokenizer's decode function 
Author: bugface
Pull request: no

input example:
```python
tok_ids = [ 101,  2632,  1011,  7110,  1010,  2142,  5424, 14041,  2727,  1011,  2260,  1011,  5757,   102,   0]
```

if we do decode
```python
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenizer.decode(tok_ids, clean_up_tokenization_spaces=False)

# output is 
'[CLS] al - ain , united arab emirates 1996 - 12 - 06 [SEP] [PAD]'
# we can see here the ',' is separated from word 'ain'
```

if we do decode
```python
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
tokenizer.decode(tok_id, clean_up_tokenization_spaces=False)

# output is
'[CLS] al - ain, united arab emirates 1996 - 12 - 06 [SEP] [PAD]'
# the space between ',' and 'ain' is removed although we set clean_up_tokenization_spaces as False
```

following the above question, I find another inconsistent behavior:

```python
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenizer.decode(tokenizer.encode("al-ain"))
# I expect decode result is "al-ain" but get "al - ain", so spaces are inserted
```

Is this designed as encoding and decoding the same word can lead to different output or it is a bug?

more precisely, if we do
```python
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenizer.tokenize("al-ain")
# output is ['al', '-', 'ain']
```
but I expect it generates subwords not inserting spaces.

Comments:
cc @SaulLu 

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@SaulLu is this issue resolved or WIP ?

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Hi @bugface,

> Is this designed as encoding and decoding the same word can lead to different output or it is a bug?

Short answer: yes :smiley: 

In general, the `decode` method is a best-effort method because in the `text -> encode -> sequence of ids` operation there may be some loss of information (and this is the case for Bert's tokenizer by design). In general this is not a big deal as Bert is not a generative model and therefore does not need to decode a purely generated id sequence (I'd be very interested to know what use case you use decode for - as this is the subject of quite a few issues on our side! :pray: )

Also regarding the `clean_up_tokenization_spaces`, this argument targets specific cases as you can see in the method used to perform this cleaning:
https://github.com/huggingface/transformers/blob/f394a2a50d8729cd1ca9b368e330ec50664c3292/src/transformers/tokenization_utils_base.py#L3392-L3415

IMO, the `-` case is less "common" than the one listed in the current cleaning script.

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Is this really the difference of AutoTokenizer/BertTokenizer, or it's BertTokenizer vs. BertTokenizerFast?