Switch from using sum for flattening lists of lists in group_texts
# Speed up list flattening in `group_texts`  by changing `sum(list_of_lists, [])` to `functools.reduce(operator.iconcat, list_of_lists, [])`

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I changed all list flattening from `sum(list_of_lists, [])` to `functools.reduce(operator.iconcat, list_of_lists, [])`. 

Here is a stack overflow thread about which method is fastest: https://stackoverflow.com/a/45323085

Here is a colab notebook that shows a quick example between the old way and the new way and a couple of timed examples. The new way is about 5-6x faster. https://colab.research.google.com/drive/1Kxj_JbM9HMLFpjUduy6i3tfqDob_pYIp?usp=sharing

I discovered this while trying to use `group_texts` on many GB of data, and the speedup was greatly appreciated. 

Nearly all of these changes are in `run_mlm` or `run_clm` examples, but there are a couple in `run_swag` and another 
 in `file_utils.py` which might be unnecessary.

I don't know why `make style` is moving `import functools` to its own line above the other imports in examples/flax/language-modeling/run_t5_mlm_flax.py and examples/tensorflow/language-modeling/run_clm.py

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I think @sgugger wrote the original `group_texts`



Thanks for investigating the performance of that line! I initially used the `sum()` because it's easy to read. The `functools.reduce(operator.iconcat, list_of_lists, [])` is way less readable, but it gives so much in speed that switching may be a good thing.

Wdyt @LysandreJik ?<|||||>I was actually confused at first because I didn't know that `sum()` could be used to flatten a list of lists like that. Perhaps just adding a comment explaining that it is flattening the list of lists into one list will suffice? Something along the lines of, "This concatenates all sequences together by flattening the list of lists"

Alternatively, would `chain.from_iterable(list_of_lists)` or `chain(*list_of_lists)` seem more readable?<|||||>I think `chain(*list_of_lists)` is the most readable, and is even clearer than the `sum` thing.
It makes almost no difference in time from your notebook, so let's go with this one?<|||||>Ok I'll make the changes. Do you know why `make style` moved `import functools` into its own line in `examples/flax/language-modeling/run_t5_mlm_flax.py` and `examples/tensorflow/language-modeling/run_clm.py`?<|||||>I did a couple more tests in this notebook: https://colab.research.google.com/drive/1Kxj_JbM9HMLFpjUduy6i3tfqDob_pYIp


Edit: This actually didn't work. Let me try to fix it.
~~One way to improve readability would be to make a utility function like this: `ravel = functools.partial(functools.reduce, operator.iconcat, [])` so then you could just use `ravel(x)` inside `group_texts`.~~

This works: `ravel = functools.partial(functools.reduce, operator.iconcat)` so then you could just use `ravel(x, [])` inside `group_texts`.   `ravel` would mirror what `torch.ravel` and `np.ravel` do. I'm not sure if that is more or less confusing.

Edit: it is same with/without partial
~~Edit: double-checking this right now~~
~~This method actually performed the fastest out of everything I tried.~~

Here is a summary of the methods when using `group_texts` on SQuAD contexts where `x` is a list of lists (each time is a 'best of 5' except `sum` which is a 'best of 3'):

1. `sum(x, [])` - 2min 47s
2. `list(chain.from_iterable(x))` - 27.2 s
3. `list(chain(*x))` - 27.1 s 
4. `functools.reduce(operator.iconcat, x, [])` - 26.8 s
5. `functools.partial(functools.reduce, operator.iconcat)(x, [])`  - 26.8 s
6. `np.ravel(x)` - 28.9 s 
7. `[b  for a in x for b in a]` - 28.4 s per loop

<|||||>I think that option 3 (`list(chain(*x))`) is the best compromise in terms of readability vs speed (only 3ms longer than the best run, which might also be dataset-dependent).

Thanks a lot for benchmarking all options!