TextDatasetForNextSentencePrediction does not seem to contain truncate function unlike LineByLineWithSOPTextDataset
## Environment info
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- `transformers` version: 4.6.1
- Platform: Ubuntu 20.04.2 LTS
- Python version: 3.8.5
- PyTorch version (GPU?): 1.8.1 (Yes)
- Tensorflow version (GPU?): Not installed (NA)
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No

### Who can help
@LysandreJik
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## Information

Model I am using (Bert, XLNet ...): Bert(PreTraining)

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

The tasks I am working on is:
* [x] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:

1. Prepare input.txt like:
```
About this time, when some rain began to fall, Sancho proposed that they should shelter themselves in the fulling-mill, but Don Quixote had conceived such abhorrence for it, on account of what was past, that he would no means set foot within its wall; wherefore, turning to the right-hand, they chanced to fall in with a road different from that in which they had traveled the day before; they had not gone far, when the knight discovered a man riding with something on his head, that glittered like polished gold, and scarce had he descried this phenomenon, when turning to Sancho, “I find,” said he, “that every proverb is strictly true; indeed, all of them are apophthegms dictated by experience herself; more especially, that which says, “shut one door, and another will soon open”: this I mention, because, if last night, fortune shut against us the door we fought to enter, by deceiving us with the fulling-hammers; today another stands wide open, in proffering to use us, another greater and more certain adventure, by which, if I fail to enter, it shall be my own fault, and not imputed to my ignorance of fulling-mills, or the darkness of the night.
About this time, when some rain began to fall, Sancho proposed that they should shelter themselves in the fulling-mill, but Don Quixote had conceived such abhorrence for it, on account of what was past, that he would no means set foot within its wall; wherefore, turning to the right-hand, they chanced to fall in with a road different from that in which they had traveled the day before; they had not gone far, when the knight discovered a man riding with something on his head, that glittered like polished gold, and scarce had he descried this phenomenon, when turning to Sancho, “I find,” said he, “that every proverb is strictly true; indeed, all of them are apophthegms dictated by experience herself; more especially, that which says, “shut one door, and another will soon open”: this I mention, because, if last night, fortune shut against us the door we fought to enter, by deceiving us with the fulling-hammers; today another stands wide open, in proffering to use us, another greater and more certain adventure, by which, if I fail to enter, it shall be my own fault, and not imputed to my ignorance of fulling-mills, or the darkness of the night.

```
(I think any document where the total number of tokens when using TextDatasetForNextSentencePrediction exceeds 512 will be fine)

2. Run code below:
```python
import transformers
from transformers.data.datasets import TextDatasetForNextSentencePrediction
from transformers.data.data_collator import DataCollatorForLanguageModeling
from transformers import BertConfig, BertForPreTraining, Trainer, BertTokenizer

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

training_args = transformers.TrainingArguments(
            output_dir = './bert/',
            per_device_train_batch_size = 2
)

train_dataset = TextDatasetForNextSentencePrediction(
    tokenizer = tokenizer, 
    file_path = 'input.txt', 
    overwrite_cache= True,
    block_size = 512,
)

data_collator = DataCollatorForLanguageModeling(
            tokenizer = tokenizer,
            mlm = True,
)

bert_config = BertConfig(
    vocab_size = tokenizer.vocab_size, 
    hidden_size = 768, 
    num_attention_heads = 12
)
model = BertForPreTraining(config=bert_config)

trainer = Trainer(
        model = model,
        args = training_args,
        data_collator = data_collator,
        train_dataset = train_dataset,
)
trainer.train()
```

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## Expected behavior
No error is expected, but got an error:
```
RuntimeError                              Traceback (most recent call last)
<ipython-input-2-ddc701df65e7> in <module>
     32 
     33 )
---> 34 trainer.train()

~/j-fin-bert/venv/lib/python3.8/site-packages/transformers/trainer.py in train(self, resume_from_checkpoint, trial, **kwargs)
   1270                         tr_loss += self.training_step(model, inputs)
   1271                 else:
-> 1272                     tr_loss += self.training_step(model, inputs)
   1273                 self.current_flos += float(self.floating_point_ops(inputs))
   1274 

~/j-fin-bert/venv/lib/python3.8/site-packages/transformers/trainer.py in training_step(self, model, inputs)
   1732                 loss = self.compute_loss(model, inputs)
   1733         else:
-> 1734             loss = self.compute_loss(model, inputs)
   1735 
   1736         if self.args.n_gpu > 1:

~/j-fin-bert/venv/lib/python3.8/site-packages/transformers/trainer.py in compute_loss(self, model, inputs, return_outputs)
   1764         else:
   1765             labels = None
-> 1766         outputs = model(**inputs)
   1767         # Save past state if it exists
   1768         # TODO: this needs to be fixed and made cleaner later.

~/j-fin-bert/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/j-fin-bert/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, next_sentence_label, output_attentions, output_hidden_states, return_dict)
   1067         return_dict = return_dict if return_dict is not None else self.config.use_return_dict
   1068 
-> 1069         outputs = self.bert(
   1070             input_ids,
   1071             attention_mask=attention_mask,

~/j-fin-bert/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/j-fin-bert/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)
    962         head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
    963 
--> 964         embedding_output = self.embeddings(
    965             input_ids=input_ids,
    966             position_ids=position_ids,

~/j-fin-bert/venv/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    887             result = self._slow_forward(*input, **kwargs)
    888         else:
--> 889             result = self.forward(*input, **kwargs)
    890         for hook in itertools.chain(
    891                 _global_forward_hooks.values(),

~/j-fin-bert/venv/lib/python3.8/site-packages/transformers/models/bert/modeling_bert.py in forward(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)
    205         if self.position_embedding_type == "absolute":
    206             position_embeddings = self.position_embeddings(position_ids)
--> 207             embeddings += position_embeddings
    208         embeddings = self.LayerNorm(embeddings)
    209         embeddings = self.dropout(embeddings)

RuntimeError: The size of tensor a (555) must match the size of tensor b (512) at non-singleton dimension 1
```

I think this is due to the fact that TextDateForNextSentencePrediction, unlike LineByLineWithSOPTextDataset, does not have the truncate feature like truncate_seq_pair in the create_examples_from_document function.

So I added truncate_seq_pair such as 
https://github.com/huggingface/transformers/blob/802ffaff0da0a7d28b0fef85b44de5c66f717a4b/src/transformers/data/datasets/language_modeling.py#L293-L310
to 
https://github.com/huggingface/transformers/blob/802ffaff0da0a7d28b0fef85b44de5c66f717a4b/src/transformers/data/datasets/language_modeling.py#L491-L492

Then it worked.

Should truncate_seq_pair be also added in TextDatasetForNextSentencePrediction?


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