Use cached added-token dicts in per-token decode loops

[ishan-1010]

What this fixes

Fixes #46396 (follow-up to #46323). Four slow tokenizers read the added_tokens_encoder / added_tokens_decoder properties inside the per-token loop of convert_tokens_to_string. Both properties rebuild and re-sort the full added-token map on every access, so decoding T tokens with N added tokens costs O(T * N * logN).

The fix

Point those loops at the maintained _added_tokens_encoder / _added_tokens_decoder dicts instead, same as #46323 did for convert_tokens_to_ids. Membership and lookup don't depend on the property's sort order, so behavior is unchanged. Files: byt5, myt5, dia, perceiver tokenizers; 10 lines changed, attribute swaps only.

This is option (B) from #46396. The broader option (memoizing the property itself) is left for that issue since it has the cpmant-mutation and sort-order questions.

Numbers

Decoding 2,840 mixed tokens with 4,000 added tokens (M1, CPU):

tokenizer	before	after
ByT5	2,207 ms	0.6 ms
Perceiver	1,270 ms	0.6 ms

Output verified byte-identical (md5) before/after for both.

Tests

python -m pytest tests/models/byt5/test_tokenization_byt5.py tests/models/myt5/test_tokenization_myt5.py tests/models/perceiver/test_tokenization_perceiver.py
# 78 passed, 107 skipped, 6 failed (same 6 fail on unmodified main; network/torch integration tests)
python -m pytest tests/models/dia/test_tokenization_dia.py
# 37 passed, 24 skipped

Notes

Not a duplicate: no open PR touches these call sites (checked per the contribution policy). AI-assisted; I reviewed every line and ran the tests and benchmark above.

[Rocketknight1]

cc @itazap

[itazap]

thanks!

[HuggingFaceDocBuilderDev]

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[github-actions]

[For maintainers] Suggested jobs to run (before merge)

run-slow: byt5, dia, myt5, perceiver