Fixing failure when labels have different length
Pop out label when padding the signal, add it back afterward to avoid the failure caused by different label length.

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The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_19940). All of your documentation changes will be reflected on that endpoint.<|||||>Thanks for your PR. I don't see what is failing with the current code, could you provide a reproducible example of a bug?<|||||>This is a minimum example:
```python
import numpy as np
import datasets
from transformers import AutoTokenizer, BertForTokenClassification,DataCollatorForTokenClassification,Trainer,TrainingArguments
def tokenize_function(examples):
    input = tokenizer(examples["tokens"], is_split_into_words=True, truncation=True)
    return input

raw_datasets = datasets.load_dataset('conllpp')
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
n_label = len(raw_datasets['train'].features['ner_tags'].feature.names)
model = BertForTokenClassification.from_pretrained('bert-base-uncased',num_labels=n_label)
tokenized_datasets = raw_datasets.map(tokenize_function, 
                                      batched=True)
tokenized_datasets = tokenized_datasets.rename_column("ner_tags", "labels")
tokenized_datasets.set_format("torch") 
#This will make the collector to use the torch_call function and lead to failure when the label has different length.
data_collator = DataCollatorForTokenClassification(tokenizer)
training_args = TrainingArguments(output_dir = "bert_finetune", 
                                  evaluation_strategy = "epoch", 
                                  save_strategy="epoch",
                                  learning_rate=1e-5, 
                                  num_train_epochs=1, 
                                  per_device_train_batch_size=16, 
                                  per_device_eval_batch_size=16, 
                                  weight_decay=0.01, 
                                  push_to_hub = False)
trainer = Trainer(model,
                  training_args,
                  train_dataset=tokenized_datasets['train'],
                  eval_dataset=tokenized_datasets['validation'],
                  data_collator=data_collator,
                  tokenizer=tokenizer)
trainer.train()
```
The error:
Traceback (most recent call last):
  File "collector_issue.py", line 40, in <module>
    trainer.train()
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/trainer.py", line 1500, in train
    return inner_training_loop(
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/trainer.py", line 1716, in _inner_training_loop
    for step, inputs in enumerate(epoch_iterator):
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 628, in __next__
    data = self._next_data()
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 671, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 61, in fetch
    return self.collate_fn(data)
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/data/data_collator.py", line 42, in __call__
    return self.torch_call(features)
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/data/data_collator.py", line 306, in torch_call
    batch = self.tokenizer.pad(
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 2981, in pad
    return BatchEncoding(batch_outputs, tensor_type=return_tensors)
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 206, in __init__
    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
  File "/home/haotiant/anaconda3/envs/nlp/lib/python3.8/site-packages/transformers/tokenization_utils_base.py", line 732, in convert_to_tensors
    raise ValueError(
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length. Perhaps your features (`labels` in this case) have excessive nesting (inputs type `list` where type `int` is expected).

Which is caused by torch_call function in transformers/data/data_collator.py
Specifically:
https://github.com/huggingface/transformers/blob/2e35bac4e73558d334ea5bbf96a1116f7c0d7fb3/src/transformers/data/data_collator.py#L307-L313

Notice the comment "# Conversion to tensors will fail if we have labels as they are not of the same length yet."
So I just fixed this bug by poping out the labels first and then feed them back, and then the afterward code would take care of the padding.<|||||>You have not aligned your TAGS with the tokens in this example, so it won't work anyway.<|||||>The error is due to the nested label list which has different lengths. I am not sure what you mean by aligning TAGS with token, I supposed you mean padding the label so they would have same length, but wasn't the collector supposed to do the auto-padding given nested a nested list label?<|||||>The collator will do the padding of the labels to add as many pad tokens as in the inputs. But the tokenizer splits a word in multiple subwords, and you haven't done anything for that in your labels. So you still end up with labels being of different sizes.<|||||>I think the label padding is after the tokenizer padding:

https://github.com/huggingface/transformers/blob/2e35bac4e73558d334ea5bbf96a1116f7c0d7fb3/src/transformers/data/data_collator.py#L320-325

And the padding is dynamically added for every batch, so I think there will be no problem?<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.