TextVectorization: adapt() with max_tokens vs. a fixed vocabulary for reproducible token indices

I'm designing a text preprocessing pipeline with tf.keras.layers.TextVectorization where token indices must stay identical across training sessions, because downstream checkpoints and a transfer-learning step depend on a stable mapping. Assume a recent TensorFlow 2.x release.

My understanding is that adapt() builds the vocabulary from token frequencies, and when max_tokens truncates the list, ties between equally frequent tokens may be resolved in a way that isn't documented as deterministic—especially on large or distributed datasets. The alternative is to skip adapt() entirely and pass an explicit, sorted vocabulary list at layer construction, which guarantees order but means I maintain the token list myself and lose the convenience of frequency-based pruning.

I've also read that manually sorting the vocabulary after adapt() can break the layer's internal mapping, so that doesn't seem like a safe middle ground.

Given the constraint of bit-identical token indices across runs and machines:

• Is the non-determinism in adapt() vocabulary ordering documented behavior, and does tf.config.experimental.enable_op_determinism() affect it?
• Is supplying a precomputed fixed vocabulary the intended approach for reproducible pipelines, or is there a supported way to make adapt() deterministic?
• Are there trade-offs of the fixed-vocabulary route (e.g., OOV handling, max_tokens interaction) I should account for?