Updating linen_examples/seq2seq

[andsteing]

This issue tracks updates the seq2seq example to follow practices outlined in #231.

• Port to linen API - once ported all subsequent changes should be done in linen_examples/seq2seq
• Update README.md
• Add requirements.txt Improves seq2seq example #1396
• Update file structure
• Use ml_collections.ConfigDict ?
• Add benchmark test ?
• Add unit test for training/eval step ?
• Add Colab Improves seq2seq example #1396
• Adhere to Google Python style
• Add mypy annotations
• Shorten/beautify training loop (consider using clu for this)

[andsteing]

Note that "file structure" in #231 got changed (as part of #634):

• To make it easier to test, maintain and reuse code structure the code as follows:

  • main.py contains the flags and calls a method from train.py to run the training loop. This should be the only file defining flags! This can be almost identical for all examples, please copy from linen_examples/wmt/main.py.
  • train.py contains classes and methods for training and evaluating the model.
  • train_test.py for test cases of the training code. At a minimum the test should run a single training step but more fine grained unit tests are a bonus. You can use tfds.testing.mock_data to avoid real data from disk.

[andsteing]

as discussed in last week's meeting, we don't really need a benchmark/colab in every example, we don't require mypy annotations, and very simple examples don't need configs.