`BackTranslationAugmenter` produces an incorrect number of transformations

[aemartinez]

Describe the bug
Calling the augment function of a BackTranslationAugmenter produces a list with a length that appears to be non-deterministic, sometimes not matching the value of the transformations_per_example initialization parameter.

To Reproduce
Run the following code:

import textattack
augmenter = textattack.augmentation.recipes.BackTranslationAugmenter(transformations_per_example=5)
s = "You are in the kitchen."
ls = augmenter.augment(s)
print(len(ls))

Expected behavior
Expected output: 5
Actual output: 3

System Information (please complete the following information):

• OS: MacOS Sonoma 14.6.1
• Library versions: torch==2.3.1, transformers==4.42.4
• Textattack version: textattack==0.3.10
• Full pip freeze output: https://pastebin.com/VPt7JdRY

[qiyanjun]

Root cause: Augmenter.augment accumulates results in a set() but only ever attempts exactly transformations_per_example outer-loop calls to the transformation. BackTranslationAugmenter defaults to chained_back_translation=5 (randomly chained through 5 languages), and for a short sentence like yours, two of those attempts can easily land on the identical final phrasing — the set silently collapses the duplicate, so you get fewer results than requested. It's not really about back-translation specifically; any transformation with limited output diversity for a given input hits this.

Opened #831 with a bounded retry (up to 3x the requested count) so the loop backfills collisions instead of accepting a short result. Transformations with large search spaces (the common case) see zero behavior change. Will close automatically once that merges.