Support dill 0.3.6
State: closed | Author: albertvillanova (MEMBER)

This PR:
- ~~Unpins dill to allow installing dill>=0.3.6~~
- ~~Removes the fix on dill for >=0.3.6 because they implemented a deterministic mode (to be confirmed by @anivegesana)~~
- Pins dill<0.3.7 to allow latest dill 0.3.6
- Implements a fix for dill `save_function` for dill 0.3.6
- Additionally had to implement a fix for dill `save_code` and `_save_regex` for dill 0.3.6
- Fixes the CI so that the latest dill version is tested (besides the minimum 0.3.1.1 required by apache-beam 2.42.0)

Fix #5162.

Comments: _The documentation is not available anymore as the PR was closed or merged._

I think it hasn't been merged ? https://github.com/uqfoundation/dill/pull/501

Though I can see that the CI is green because it uses dill 0.3.1.1 - we should probably fix the dill version in both CIs:
- use 0.3.1.1 for the CI with the minimum requirements
- use latest for the CI with the latest requirements

I have noticed our CI uses `dill-0.3.1.1`, so not really testing dill 0.3.6...

The dill version in our CI is due to `apache-beam`...

I've tested locally: we need a specific fix for 0.3.6 (different from the previous ones)...

I think we can force the version of dill to be whatever we want in the CI - no matter what beam says. The alternative would be to run beam tests separately but it's more work

@lhoestq I tried the easiest solution: force dill==0.3.6 ignoring the requirement of apache-beam. But it doesn't work:
- For example, for `tests/test_builder.py::test_beam_based_builder_download_and_prepare_as_parquet`:
```
    @dill.dill.register(dill.dill.ModuleType)
    def save_module(pickler, obj):
      if dill.dill.is_dill(pickler) and obj is pickler._main:
        return old_save_module(pickler, obj)
      else:
>       dill.dill.log.info('M2: %s' % obj)
E       AttributeError: module 'dill._dill' has no attribute 'log'

venv/lib/python3.9/site-packages/apache_beam/internal/dill_pickler.py:170: AttributeError
```
  - Apache Beam registers some dill functions (`save_module`) which are incompatible with dill 0.3.6 (in 0.3.6 'dill._dill' has no attribute 'log' but 'logger')
  - This has an impact in CI tests using either Apache Beam or `multiprocess` (even without using Apache Beam!):
```
FAILED tests/test_beam.py::BeamBuilderTest::test_download_and_prepare - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_beam.py::BeamBuilderTest::test_nested_features - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_multiprocessing_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_filter_multiprocessing_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_builder.py::test_beam_based_download_and_prepare - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_builder.py::test_beam_based_as_dataset - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_in_memory - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_multiprocessing_on_disk - AttributeError: module 'dill._dill' has no attribute 'log'
FAILED tests/test_builder.py::test_beam_based_builder_download_and_prepare_as_parquet - AttributeError: module 'dill._dill' has no attribute 'log'
```

I guess we should implement the other option: run beam tests separately.

I'm opening another PR for the CI refactoring.

Ah crap >< maybe only install apache_beam for the "minimum requirements" CI

@lhoestq if we install apache-beam only in the "minimum requirements" CI, then this other PR should be merged first:
- #5168 

Otherwise, our CI for "latest" will fail because it will try to run the beam tests (because PyTorch is installed but indeed apache-beam is not installed).

One of the test is failing because we set 
```python
# google colab doesn't allow to pickle loggers
# so we want to make sure each tests passes without pickling the logger
def reduce_ex(self):
    raise pickle.PicklingError()

datasets.arrow_dataset.logger.__reduce_ex__ = reduce_ex
```
in `test_arrow_dataset.py` to avoid pickling the logger because it used to fail on google colab.

Now pickling the logger seems to be working on google colab again - so you can remove it, and it should fix some tests

For the other 2 errors:
- FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_in_memory - _pickle.PicklingError: Can't pickle <class 'unittest.mock.MagicMock'>: it's not the same object as unittest.mock.MagicMock
- FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_map_caching_on_disk - _pickle.PicklingError: Can't pickle <class 'unittest.mock.MagicMock'>: it's not the same object as unittest.mock.MagicMock

I have implemented a pickable MagicMock.