batch_encode_plus with pad_to_max_length but no max_length is not padding the output
# 🐛 Bug

## Information

Model I am using BERT:

Language I am using the model on (English, Chinese ...): English

The problem arises when using:
* [ ] the official example scripts: (give details below)
* [x] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:                                                                                                                                             
```
import torch
import numpy as np
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

seq1 = "This is a short sequence"
seq2 = "This will be a much longer sequence, so the short one requires padding"

input = [seq1, seq2]

# Explicitly specified padding length
max_len = 20
tck_temp = tokenizer.batch_encode_plus(input, max_length=max_len, pad_to_max_length=True)
inp_ids = tck_temp['input_ids']
assert len(inp_ids[0]) == len(inp_ids[1]) == max_len, "Both inputs should have length equal to 20"

# Implicit padding length set to models max length
model_max_len = tokenizer.max_len
tck_temp = tokenizer.batch_encode_plus(input, pad_to_max_length=True)inp_ids = tck_temp['input_ids']
assert len(inp_ids[0]) == len(inp_ids[1]) == model_max_len, "Both inputs should have length equal to %d" % model_max_len
```

## Expected behavior
According to the documentation, `batch_encode_plus` with `pad_to_max_length=True` should pad sequence to models maximal length, if the `max_length` is not explicitly specified.
The attached script should run without raising Exception.

From documentation
"If no max length is specified, the padding is done up to the model’s max length."

## Environment info
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.7.0
- Platform: Linux-4.15.0-74-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.4.0 (False)
- Tensorflow version (GPU?): N/A
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

Hi,

Is anyone working on this or is it open for someone to take?
I was able to reproduce the issue.
If not being worked on by anyone, I would like to take it up.

Thanks<|||||>I'm facing similar issues with batch_encode_plus:
```
tokenizer = transformers.BertTokenizer.from_pretrained('bert-base-cased')
a = ['short sentence', 'Larger sentence than short sentence']
input_ids = torch.tensor(tokenizer.batch_encode_plus(a, pad_to_max_length=True)['input_ids'])
```
It doesn't work for me, it return this error:
`ValueError: expected sequence of length 2 at dim 1 (got 6)`

Thanks<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
<|||||>This should now be fixed on master with the updated tokenizer API