[DataCollator] Warn on identical `eos_token_id` and `pad_token_id`
Author: MustSave
Pull request: yes

# What does this PR do?
This PR displays a warning message when the values of `pad_token_id` and `eos_token_id` are identical. This is to prevent unexpected behavior during multi-turn training.

After the multi-turn data training with [DataCollatorForCompletionOnlyLM](https://github.com/huggingface/trl/blob/9e9f024399b76842ece3552884bbc4f304fd4153/trl/trainer/utils.py#L56), I encountered an issue where the model continued generating outputs even after the assistant's turn had been completed. This issue was due to the equivalence of the tokenizer's eos token and pad token by default, resulting in the eos token not being properly trained.

For instance, in the torch_call() function within [data_collator.py](https://github.com/huggingface/transformers/blob/7ee995fd9c692761c4601ddbffa2ac2ec9f27b0b/src/transformers/data/data_collator.py#L740C10-L740C10), the pad_token_id is converted to ignore_id(-100).

```python
if self.mlm:
    batch["input_ids"], batch["labels"] = self.torch_mask_tokens(
        batch["input_ids"], special_tokens_mask=special_tokens_mask
    )
else:
    labels = batch["input_ids"].clone()
    if self.tokenizer.pad_token_id is not None:
        labels[labels == self.tokenizer.pad_token_id] = -100
    batch["labels"] = labels
```

If the multi-turn data is formatted as shown below, and `eos_token_id` and `pad_token_id` are identical, the eos_token would not be properly trained. This could lead to a scenario where the model continuously generates both user and assistant turns without recognizing the end of sequence (eos) token.
```

### User: What is up?
### Assistant: Hello! How can I help you today?

### User: Goodbye
### Assistant: Goodbye! If you have any more questions in the future, don't hesitate to ask.

```

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- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

@muellerz @pacman100

Comments:
> Hi @MustSave - thanks for opening this PR!
> 
> This warning should be included in the data collator responsible for collating for multi-turn learning, rather than the parent class.

Thank you for your comment. I will make the modifications to include a warning in the relevant class.