RoFormerTokenizerFast has a wrong result when setting "return_offsets_mapping=True"
Author: JaheimLee
Pull request: no

I use roformer_chinese_char_base  model, so there is no word-level problem. RoFormerTokenizerFast has this bug, but BertTokenizerFast doesn't. Here is the code:
```python
In [1]: from transformers import RoFormerTokenizerFast, BertTokenizerFast

In [2]: path = '/data/pretrained_models/roformer_chinese_char_base'

In [3]: tokenizer_roformer = RoFormerTokenizerFast.from_pretrained(path, add_special_tokens=False, do_lower_case=True)

In [4]: tokenizer_bert = BertTokenizerFast.from_pretrained(path, add_special_tokens=False, do_lower_case=True)

In [5]: text = '收到真的很喜欢，真的是大爱，平常上个网打个游戏，查个东西都非常的好，网速也很快，真是淘到宝了'

In [6]: tokenizer_bert(text, return_offsets_mapping=True, add_special_tokens=False)
Out[6]: {'input_ids': [2684, 691, 4395, 4334, 2100, 1134, 3223, 7675, 4395, 4334, 2798, 1465, 3898, 7675, 1975, 1960, 198, 223, 5079, 2388, 223, 3602, 2349, 7675, 2982, 223, 214, 6017, 6657, 7160, 1960, 4334, 1506, 7675, 5079, 6552, 260, 2100, 2148, 7675, 4395, 2798, 3554, 691, 1698, 270], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'offset_mapping': [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (12, 13), (13, 14), (14, 15), (15, 16), (16, 17), (17, 18), (18, 19), (19, 20), (20, 21), (21, 22), (22, 23), (23, 24), (24, 25), (25, 26), (26, 27), (27, 28), (28, 29), (29, 30), (30, 31), (31, 32), (32, 33), (33, 34), (34, 35), (35, 36), (36, 37), (37, 38), (38, 39), (39, 40), (40, 41), (41, 42), (42, 43), (43, 44), (44, 45), (45, 46)]}

In [7]: tokenizer_roformer(text, return_offsets_mapping=True, add_special_tokens=False)
Out[7]: {'input_ids': [2684, 691, 4395, 4334, 2100, 1134, 3223, 7675, 4395, 4334, 2798, 1465, 3898, 7675, 1975, 1960, 198, 223, 5079, 2388, 223, 3602, 2349, 7675, 2982, 223, 214, 6017, 6657, 7160, 1960, 4334, 1506, 7675, 5079, 6552, 260, 2100, 2148, 7675, 4395, 2798, 3554, 691, 1698, 270], 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'offset_mapping': [(0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1), (0, 1)]}
```
As you can see, "offset_mapping" is wrong at Out[7].

@JunnYu

Comments:
@JaheimLee 
**uncomment** 取消注释 L44-L53
##### this code slice normalized_string is too slow (6s) but test_alignement_methods can pass

https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/src/transformers/models/roformer/tokenization_utils.py#L43-L53

If we use this code. `offset_mapping` is true but it will take a lot of processing time.

------------------------------------------------------------------------------------------------------------
and **comment** 注释 L56-L63
##### this code test_alignement_methods can't pass but fast (300ms)
https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/src/transformers/models/roformer/tokenization_utils.py#L55-L63

If we use this code. `offset_mapping` is wrong but it will take very little processing time.

------------------------------------------------------------------------------------------------------------

If you use `char level` model , recommend you to use BertTokenizer. (the speed is very fast)

And if you use `word level` model like `roformer_chinese_base`, recommend you to use RoFormerTokenizer. （if you don't care `speed` and want to get true `offset_mapping`, you should **uncomment**  L44-L53 and **comment** L56-L63 in transformers/src/transformers/models/roformer/tokenization_utils.py）