fix ZeroDivisionError: division by zero
Author: JackKuo666
Pull request: yes

The logic of if len_dataloader is not None should be to determine whether train_dataset is IterableDataset，But whether train_dataset is IterableDataset or not, len dataloader is 1，so, it should be change `if not isinstance(self.train_dataset, torch.utils.data.IterableDataset)`

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Fixes #17350 (issue)


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Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_17351). All of your documentation changes will be reflected on that endpoint.

No, this is incorrect. Some `IterableDataset` do have a length and we don't want this specific check here. You did not share your code in your issue, but it looks like the length of your custom iterable dataset is wrong.

> No, this is incorrect. Some `IterableDataset` do have a length and we don't want this specific check here. You did not share your code in your issue, but it looks like the length of your custom iterable dataset is wrong.

my custom iterable dataset is :
```py

class CustomIterableDataset(torch.utils.data.IterableDataset):
    
    def __init__(self, tokenizer, data_file, num_lines, block_size):
        self.data_file = data_file
        self.block_size = block_size
        self.num_lines = num_lines
        self.tokenizer = tokenizer
        if num_lines == -1:
            raise Exception("请在--data_lines输入数据的行数")
    
    def __len__(self):
        return self.num_lines
    
    def __iter__(self):
        while True:
            with open(self.data_file, 'rt', encoding='utf-8') as f:
                for line in f.readline():
                    line = f.readline().strip()
                    batch_encoding = self.tokenizer.batch_encode_plus(line, add_special_tokens=True, max_length=self.block_size)
                    self.examples = batch_encoding["input_ids"]
                    yield torch.tensor(self.examples[0], dtype=torch.long)
```
i think if we set `max_steps=total_line//pretrain_batch_size` ,in `trainer`, that should correct?
```py
    training_args = TrainingArguments(
        output_dir=args.save_dir, overwrite_output_dir=True, num_train_epochs=num_train_epochs, #  max_steps=total_line//pretrain_batch_size ,
        learning_rate=1e-4, weight_decay=0.01, warmup_steps=10000, local_rank = args.local_rank, 
        per_device_train_batch_size=pretrain_batch_size,logging_steps=500, save_total_limit = 1, logging_dir="./runs",
        load_best_model_at_end=True, save_strategy="epoch", evaluation_strategy="epoch",
        metric_for_best_model="loss")

```


If you pass along `max_steps`, it will override the rest, yes.

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