Correct order of overflowing_tokens for slow tokenizer
# What does this PR do?
 When using a slow tokenizer (LayoutLM, Bert, Alberta, etc.), the `overflowing_tokens` were obtained in the wrong order. I have made the necessary changes that will produce the `overflowing_tokens` in the correct order.
## Tasks summary - 
- [x] making sure overflowing tokens are returned in the correct order for all `truncation_strategy` for a sequences of input ids.
- [x] if a pair of sequences of input ids (or batch of pairs) is provided, an error should be raised for the `truncation_strategy=True` or `longest_first` stating _"Not possible to return overflowing tokens for pair of sequences with the `longest_first`.Please select another truncation strategy than `longest_first`, for instance `only_second` or `only_first`."_
- [x] Replaced the deprecated method `encode_plus` to regular `__call__` method in `test\test_tokenization_common.py`.

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This PR fixes the issue [ huggingface/transformers#13148 ](https://github.com/huggingface/transformers/issues/13148 ) 
Fixes huggingface#13148



## Before submitting
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md#start-contributing-pull-requests),Pull Request section? Yes 👍🏻
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Yes, [ huggingface/transformers#13148 ](https://github.com/huggingface/transformers/issues/13148 ) 
- [x] Did you write any new necessary tests?Yes 👍🏻  , Required tests are added in `tests/test_tokenization_common.py`
- [x] Did you make sure to update the documentation with your changes? 


Anyone in the community is free to review the PR once the tests have passed.

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@patrickvonplaten  @NielsRogge @LysandreJik @n1t0 @SaulLu 
Thank you very much for working on this PR.

Did you check the test `test_maximum_encoding_length_single_input` and `test_maximum_encoding_length_pair_input`  passed locally? :slightly_smiling_face:  

I think it would be good to have some tests to check that the tokens are in the right order now. What do you think? The first thing I see would be to complete the tests performed in methods [`test_maximum_encoding_length_single_input`](https://github.com/huggingface/transformers/blob/master/tests/test_tokenization_common.py#L846) and [`test_maximum_encoding_length_pair_input` ](https://github.com/huggingface/transformers/blob/master/tests/test_tokenization_common.py#L938) in file `test_tokenization_common.py`. 

For example, currently, sometimes we check that the content of the overflowing tokens corresponds to what we expect for the [fast tokenizers](https://github.com/huggingface/transformers/blob/master/tests/test_tokenization_common.py#L928) but not for the [slow ones](https://github.com/huggingface/transformers/blob/master/tests/test_tokenization_common.py#L937) (and [here](https://github.com/huggingface/transformers/blob/master/tests/test_tokenization_common.py#L1079) and [here](https://github.com/huggingface/transformers/blob/master/tests/test_tokenization_common.py#L1111)). 

Our goal is really to check in the tests the resulting overflowing tokens for all cases , i.e. all `TruncationStrategy` and with 1 sequence or a pair of sequences. 

Don't hesitate to tell me if you need more help to complete these tests. :slightly_smiling_face: 



<|||||>> Did you check the test `test_maximum_encoding_length_single_input` and `test_maximum_encoding_length_pair_input` passed locally?
 No. These two tests weren't passed locally either.


> I think it would be good to have some tests to check that the tokens are in the right order now.
I ran the updated code on several tokenizers to verify the result. They all passed. I will try making better test cases.

As mentioned, Firstly I will try to resolve the ` test_maximum_encoding_length_single_input` and `test_maximum_encoding_length_pair_input`.

Thank you @SaulLu.<|||||>@SaulLu I would like to make a request.
I want to know the correct order of overflowing tokens  for the test case : 

```
from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")

seq = ["hello my name is Ted Mosby ", "I am an Architect in Boston "]

encoding = tokenizer(seq[0],seq[1], padding=True, max_length=6, truncation=True, return_overflowing_tokens=True)

print(tokenizer.decode(encoding.input_ids))

print(tokenizer.decode(encoding.overflowing_tokens))
```<|||||>You make a very good point! Indeed, we have an API choice to make here. 

I'm going to list the possibilities I see here because I don't see an "ideal" solution that would fit into the current framework (i.e. a single list). I take the example you gave:
```python
from transformers import BertTokenizer

tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
seq = ["hello my name is Ted Mosby ", "I am an Architect in Boston "]
encoding = tokenizer(seq[0],seq[1], padding=True, max_length=6, truncation=True, return_overflowing_tokens=True)

print(tokenizer.decode(encoding.input_ids))
```
Output
```
[CLS] hello my [SEP] i [SEP]
```
the possibilities I see for the output of `encoding.overflowing_tokens`:
1. Concatenate in the same list the overflowing tokens of the sequence 1 and the sequence 2. As a result ``print(tokenizer.decode(encoding.overflowing_tokens))`` will return:
```
'name is Ted Mo ##sby am an Architect in Boston'
```
Advantage: the output format is the same; Disadvantage: we can't distinguish between the first and the second sequence
2. Create a tuple of 2 lists for the overflowing tokens of the sequence 1 and the sequence 2. As a result ``print(tokenizer.decode(encoding.overflowing_tokens[0]), tokenizer.decode(encoding.overflowing_tokens[1]))`` will return:
```
'name is Ted Mo ##sby' 'am an Architect in Boston'
```
Advantage: we can distinguish between the first and the second sequence; Disadvantage: the output format is not the same. This can be seen as a temporary micro-change if we ever want to standardize the API of fast and slow tokenizers in a second PR.
3. Raise an error because as many comments in the tests show, before it was not possible to return overflowing tokens for slow tokenizers with the longest_first strategy

@LysandreJik , @sgugger, @patil-suraj or @patrickvonplaten I think your point of view can be useful here. Should we change the output format ? Should we also aim to have the same behavior for the slow and fast tokenizers ? :slightly_smiling_face: <|||||>> Disadvantage: we can't distinguish between the first and the second sequence

For the above Disadvantage, The following method might resolve it -
The use of special tokens might help in this problem 
For instance: On the same test case mentioned above -
` [CLS] name is Ted Mo ##sby  [SEP] am an Architect in Boston [SEP]  `
 
@SaulLu Thank you for helping me out with the test case.<|||||>For `test_maximum_encoding_length_single_input` earlier the order was not correct for the slow tokenizer (i.e. reverse order if stride = 0 ) but now I guess we can add this line of code 

https://github.com/huggingface/transformers/blob/143738214cb83e471f3a43652617c8881370342c/tests/test_tokenization_common.py#L928 

for Slow tokenizer as well.

@SaulLu <|||||>@SaulLu @LysandreJik @NielsRogge @patrickvonplaten, could you please review the changes I have done in the code.<|||||>Hello, thank you for your PR! Could you please add some tests to ensure correct behavior? You can add them in `tests/tests_tokenization_common.py` so that all tokenizers get tested. Thank you!<|||||>> Hello, thank you for your PR! Could you please add some tests to ensure correct behavior? You can add them in `tests/tests_tokenization_common.py` so that all tokenizers get tested. Thank you!

@LysandreJik Thank you for reviewing the PR. Sure, I will add the necessary test to ensure the correct behavior.<|||||>@LysandreJik All the necessary tests have been added.<|||||>@LysandreJik, could you please review the tests I have added to the code. 
Thank you<|||||>Thanks for the ping @Apoorvgarg-creator, @SaulLu will take over and review :)<|||||>@Apoorvgarg-creator , thanks again for your work, I am trying to look at your PR quickly.

> @SaulLu I would like to make a request.
I want to know the correct order of overflowing tokens for the test case :

In the meantime, sorry for the delay, but after discussing it, for this case (pair of sequences and `longest_first` strategy) we think it would be better to return an error.<|||||>> @Apoorvgarg-creator , thanks again for your work, I am trying to look at your PR quickly.
> 
> > @SaulLu I would like to make a request.
> > I want to know the correct order of overflowing tokens for the test case :
> 
> In the meantime, sorry for the delay, but after discussing it, for this case (pair of sequences and `longest_first` strategy) we think it would be better to return an error.

So the code should raise an error message whenever we try to return overflowing tokens for a pair of sequences with the `longest_first` strategy.

And For single_input, I have corrected the order and also added the necessary tests in `test_tokenization_common.py`. Do these require any changes?

@SaulLu , Thank you for the review.<|||||><img width="1076" alt="Screenshot 2021-08-27 at 9 04 24 AM" src="https://user-images.githubusercontent.com/57873504/131067867-0f681b61-a82c-44ec-97f1-fa40b44fd3f3.png">

[Documentation/Preprocessing data]( https://huggingface.co/transformers/preprocessing.html ),Here they have mentioned when truncation_strategy  is set to 'True' it means `only_first` instead of `longest_first`.
@sgugger <|||||>> <img alt="Screenshot 2021-08-27 at 9 04 24 AM" width="1076" src="https://user-images.githubusercontent.com/57873504/131067867-0f681b61-a82c-44ec-97f1-fa40b44fd3f3.png">
> 
> [Documentation/Preprocessing data](https://huggingface.co/transformers/preprocessing.html),Here they have mentioned when truncation_strategy is set to 'True' it means `only_first` instead of `longest_first`.


Great catch. Indeed, the documentation seems to differ between the section ["Everything you always wanted to know about padding and truncation"](https://huggingface.co/transformers/preprocessing.html?highlight=truncation#everything-you-always-wanted-to-know-about-padding-and-truncation) and [the docstring of the _call__ methode of `PreTrainedTokenizerBase`](https://huggingface.co/transformers/internal/tokenization_utils.html?highlight=truncation#transformers.tokenization_utils_base.PreTrainedTokenizerBase.__call__). It would be best if you opened a dedicated issue so that we can deal with the problems separately?<|||||>> It would be best if you opened a dedicated issue so that we can deal with the problems separately?

Sure. I will make a separate issue for this.
Thank you for the reviews, @SaulLu. I will do the dedicated changes at the earliest.<|||||>> I didn't check if that the case or, did you check it?

No, I haven't. But I will go through the things you have mentioned above. <|||||>@SaulLu, All the changes that I could find in the docstring have been done.<|||||>@sgugger Thank you for reviewing the PR. I have made the changes mentioned above. Do I need to change every use case of `tokenizer.encode_plus` or only in the `test_maximum_encoding_length_pair_input`.<|||||>@LysandreJik @SaulLu, all the dedicated changes have been resolved. Could you please review the PR?
Thank you <|||||>Thanks for fixing this, great work!<|||||>@SaulLu @LysandreJik @sgugger @NielsRogge, Thank you for the guidance. It was an insightful experience and I hope to contribute more.