[Distillation] Fix eod masking + strategy refactoring

[vlad-karp]

Description

1. The current code didn't correctly mask eod tokens when they are segment delimiters - sft mode was fine, but the pretraining incorrectly included that token into prediction.
   The last token in the sample still need to predict the eod token as expected, it is the oed input token which need to be excluded from the loss.

2. The kl divergence averaging wasn't correct as well.

3. Since this logic was in anonymous labels_fn function, it is hard to unit test it.
   Since we are not longer inheriting from tunix distillation strategy, we can add it directly to our custom strategy class.
   The base strategy class has been intrduced.

Tests

A new test test_strategy_ignores_segmentation_zero_tokens() has been added to tests/post_training/unit/train_distill_test.py

Checklist

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[codecov]

Codecov Report

❌ Patch coverage is 76.47059% with 8 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
...ners/post_train/distillation/distillation_utils.py	74.19%	7 Missing and 1 partial ⚠️

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