Forked streaming datasets try to `open` data urls rather than use network
MEMBER
Thanks for reporting this second issue as well. We definitely want to make streaming datasets fully working in a distributed setup and with the best performance. Right now it only supports single process.

In this issue it seems that the streaming capabilities that we offer to dataset builders are not transferred to the forked process (so it fails to open remote files and start streaming data from them). In particular `open` is supposed to be mocked by our `xopen` function that is an extended open that supports remote files. Let me try to fix this
2022-03-28T13:01:06Z
https://github.com/huggingface/datasets/issues/3951#issuecomment-1080619847
https://api.github.com/repos/huggingface/datasets/issues/3951
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2022-03-28T13:01:06Z
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## Describe the bug
Building on #3950, if you bypass the pickling problem you still can't use the dataset. Somehow something gets confused and the forked processes try to `open` urls rather than anything else.

## Steps to reproduce the bug
```python
from multiprocessing import freeze_support

import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets
import torch.utils.data

# work around #3950
class TorchIterableDataset(datasets.IterableDataset, torch.utils.data.IterableDataset):
    pass

def _ensure_format(v: datasets.IterableDataset) -> datasets.IterableDataset:
    return TorchIterableDataset(v._ex_iterable, v.info, v.split, "torch", v._shuffling)

if __name__ == '__main__':
    freeze_support()

    ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True)
    ds = _ensure_format(ds)

    model = AutoModelForCausalLM.from_pretrained("distilgpt2")
    Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
```

## Expected results

I'd expect the dataset to load the url correctly and produce examples.

## Actual results
```
  warnings.warn(
***** Running training *****
  Num examples = 8000
  Num Epochs = 9223372036854775807
  Instantaneous batch size per device = 8
  Total train batch size (w. parallel, distributed & accumulation) = 8
  Gradient Accumulation steps = 1
  Total optimization steps = 1000
  0%|          | 0/1000 [00:00<?, ?it/s]Traceback (most recent call last):
  File "/Users/dlwh/src/mistral/src/stream_fork_crash.py", line 22, in <module>
    Trainer(model, train_dataset=ds, args=TrainingArguments("out", max_steps=1000, dataloader_num_workers=4)).train()
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/transformers/trainer.py", line 1339, in train
    for step, inputs in enumerate(epoch_iterator):
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 521, in __next__
    data = self._next_data()
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1203, in _next_data
    return self._process_data(data)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1229, in _process_data
    data.reraise()
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
    raise exception
FileNotFoundError: Caught FileNotFoundError in DataLoader worker process 0.
Original Traceback (most recent call last):
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop
    data = fetcher.fetch(index)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch
    data.append(next(self.dataset_iter))
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 497, in __iter__
    for key, example in self._iter():
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 494, in _iter
    yield from ex_iterable
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/datasets/iterable_dataset.py", line 87, in __iter__
    yield from self.generate_examples_fn(**self.kwargs)
  File "/Users/dlwh/.cache/huggingface/modules/datasets_modules/datasets/oscar/84838bd49d2295f62008383b05620571535451d84545037bb94d6f3501651df2/oscar.py", line 358, in _generate_examples
    with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
FileNotFoundError: [Errno 2] No such file or directory: 'https://s3.amazonaws.com/datasets.huggingface.co/oscar/1.0/unshuffled/deduplicated/en/en_part_1.txt.gz'

Error in atexit._run_exitfuncs:
Traceback (most recent call last):
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/multiprocessing/popen_fork.py", line 27, in poll
    pid, sts = os.waitpid(self.pid, flag)
  File "/Users/dlwh/.conda/envs/mistral/lib/python3.8/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 6932) is killed by signal: Terminated: 15. 
  0%|          | 0/1000 [00:02<?, ?it/s]
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.0.0
- Platform: macOS-12.2-arm64-arm-64bit
- Python version: 3.8.12
- PyArrow version: 7.0.0
- Pandas version: 1.4.1
