Add MLFLOW_FLATTEN_PARAMS support in MLflowCallback
# What does this PR do?

This PR add support for the environment variable `MLFLOW_FLATTEN_PARAMS`.

When first level parameters hold a dictionary value, it will be logged to MLflow as a string.  Currently, it will skip that parameter when the string exceed 250 characters. This is especially true with the task_specific_params which can end up being a long string.

Current warning message look like this:

> Trainer is attempting to log a value of "{'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4}, 'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4}, 'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6}}" for key "task_specific_params" as a parameter. MLflow's log_param() only accepts values no longer than 250 characters so we dropped this attribute.

With this PR, the warning message is updated to:
> Trainer is attempting to log a value of "{'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4}, 'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4}, 'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6}}" for key "task_specific_params" as a parameter. MLflow's log_param() only accepts values no longer than 250 characters so we dropped this attribute. You can use `MLFLOW_FLATTEN_PARAMS` environment variable to flatten the parameters and avoid this message.


When a user set the env variable with `os.environ['MLFLOW_FLATTEN_PARAMS'] = "True"`, the parameters will be properly sent to MLflow and logged as such:

```
task_specific_params.summarization.length_penalty	1.0
task_specific_params.summarization.max_length	128
task_specific_params.summarization.min_length	12
task_specific_params.summarization.num_beams	4
task_specific_params.summarization_cnn.length_penalty	2.0
task_specific_params.summarization_cnn.max_length	142
task_specific_params.summarization_cnn.min_length	56
task_specific_params.summarization_cnn.num_beams	4
task_specific_params.summarization_xsum.length_penalty	1.0
task_specific_params.summarization_xsum.max_length	62
task_specific_params.summarization_xsum.min_length	11
task_specific_params.summarization_xsum.num_beams	6
```

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

@sgugger I added a flatten_dict function in .utils/py_utils.py as it didn't seem right to add this in integrations.py.

_The documentation is not available anymore as the PR was closed or merged._<|||||>@sgugger the script `utils/check_inits.py` used for CI has a bug. Line 252:

```
submodule = short_path.replace(os.path.sep, ".").replace(".py", "")
```

This would lead to files starting by "py" to be improperly named. For example, `utils/py_utils.py` is interpreted as `utils_utils`.

A better replace() usage may be:
```
submodule = short_path.replace(".py", "").replace(os.path.sep, ".")
```<|||||>Looks like you messed the rebase a little bit and there are now commits in this PR that shouldn't be here.<|||||>Yep. I messed up that rebase...<|||||>Thanks again!