Regression: TorchIterableDataset doesn't have __len__
### System Info

- `transformers` version: 4.24.0
- Platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.7.15
- Huggingface_hub version: 0.10.1
- PyTorch version (GPU?): 1.12.1+cu113 (False)
- Tensorflow version (GPU?): 2.9.2 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: no (n/a)
- Using distributed or parallel set-up in script?: no

### Who can help?

* Git blame suggests @sgugger, @anton-l 
* Would be great if @patil-suraj tries the whole pipeline linked because there are some other issues downstream

### Information

- [ ] The official example scripts
- [X] My own modified scripts

### Tasks

- [X] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

Run the `Trainer` with a dataset with `streaming=True`, making it iterable. To make the `Trainer` `train_dataset` work with streaming, use `.with_format("torch")` (as suggested in https://github.com/huggingface/datasets/issues/2583#issuecomment-874078780 and [here](https://discuss.huggingface.co/t/using-iterabledataset-with-trainer-iterabledataset-has-no-len/15790/2?u=maxkriegers)).

A simple repro is below and in [**this colab**](https://colab.research.google.com/drive/1V5F5ut410hiJgS6c5us5iVz9Fk6JwOF1?usp=sharing)

```
model_name = "gpt2"
output_dir = "."
from transformers import GPT2Tokenizer, GPT2Model
from transformers import Trainer, TrainingArguments
from datasets import load_dataset
from transformers.data.data_collator import DataCollatorForLanguageModeling

dataset = load_dataset("rotten_tomatoes", split="train", streaming=True).shuffle(seed=42)

tokenizer = GPT2Tokenizer.from_pretrained(model_name)

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

model = GPT2Model.from_pretrained(model_name)

training_args = TrainingArguments(
    output_dir=output_dir,
    num_train_epochs=5,
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=dataset.with_format("torch")
)

trainer.train()
trainer.save_model()
```

which yields

```
ValueError                                Traceback (most recent call last)
<ipython-input-8-de14de894c00> in <module>
      8     args=training_args,
      9     data_collator=data_collator,
---> 10     train_dataset=dataset.with_format("torch")
     11 )

/usr/local/lib/python3.7/dist-packages/transformers/trainer.py in __init__(self, model, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)
    504 
    505         if train_dataset is not None and not has_length(train_dataset) and args.max_steps <= 0:
--> 506             raise ValueError("train_dataset does not implement __len__, max_steps has to be specified")
    507 
    508         if (

ValueError: train_dataset does not implement __len__, max_steps has to be specified
```


### Expected behavior

`TorchIterableDataset` should implement `__len__` but doesn't. It instead has a `.dataset_size` method.
Thanks for opening the issue. What exactly is the regression here? On which version of Transformers did it work and when did it stop working?

As the error clearly states (copying the full error message would be helpful by the way), you need to use `max_steps` in your training arguments instead of `num_train_epochs` since your dataset doesn't have a length.<|||||>Hey @sgugger, sorry for the missing information. According to [this forum post](https://discuss.huggingface.co/t/using-iterabledataset-with-trainer-iterabledataset-has-no-len/15790/2), the fix for this exact error is to cast the dataset to a torch dataset as described above. However, the error persists. I have not bisected to figure out if it really is a regression, but it seems so from the code online.<|||||>Hi @maxkrieger - in the forum post that you have provided you can see that the `training_args` already contains argument `max_steps=1e6`. In order for your sample to work correctly, you need to both set the `max_steps` argument as well as format your dataset for Pytorch. <|||||>Additionally if I am not mistaken after specifying `max_steps` argument you can drop `num_train_epochs` since it will be override anyways .<|||||>Aahh 🤦 somehow missed that parameter while reading the snippets @Bearnardd. Apologies for the lack of diligence, this is resolved.