* Remove deprecated logic and warnings
* Add back some code that seems to be important...
* Let's just add all he nllb stuff back; removing it is a bit more involved
* Remove kwargs
* Remove more kwargs
* fix seq2seq data collator to respect the given padding strategy
further added tests for the seq2seq data collator in the style of the `data_collator_for_token_classification` (pt, tf, np)
* formatting and change bool equals "==" to "is"
* add missed return types in tests
* update numpy test as it can handle unequal shapes, not like pt or tf
While using `run_clm.py`,[^1] I noticed that some files were being added
to my global cache, not the local cache. I set the `cache_dir` parameter
for the one call to `evaluate.load()`, which partially solved the
problem. I figured that while I was fixing the one script upstream, I
might as well fix the problem in all other example scripts that I could.
There are still some files being added to my global cache, but this
appears to be a bug in `evaluate` itself. This commit at least moves
some of the files into the local cache, which is better than before.
To create this PR, I made the following regex-based transformation:
`evaluate\.load\((.*?)\)` -> `evaluate\.load\($1,
cache_dir=model_args.cache_dir\)`. After using that, I manually fixed
all modified files with `ruff` serving as useful guidance. During the
process, I removed one existing usage of the `cache_dir` parameter in a
script that did not have a corresponding `--cache-dir` argument
declared.
[^1]: I specifically used `pytorch/language-modeling/run_clm.py` from
v4.34.1 of the library. For the original code, see the following URL:
acc394c4f5/examples/pytorch/language-modeling/run_clm.py.
* Update run_speech_recognition_ctc.py
Make sure all processes wait until data is saved before loading the processor from the output_dit
* Make sure all processes wait until data is saved before loading the processor from the output_dit
* Update run_speech_recognition_ctc.py
* Update run_speech_recognition_seq2seq.py
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* merge conflicts
* bos and eos in datacollator
* (temp) hardcode removal of attention mask
* freeze encoder
* actually freeze encoder
* set max length / num beams according to gen kwargs
* (temp) fix tests
* don't pop attn mask
* override return attention mask config from Hub
* Hub configs updated 🤗
* final fixes
* update type annotations
* backward comp
* Add examples telemetry
* Alternative approach
* Add to all other examples
* Add to templates as well
* Put framework separately
* Same for TensorFlow
