[YeDonS]

When policy_preprocessor.json or policy_postprocessor.json is malformed, so101_patch_model_config.py can rewrite earlier checkpoint files and create backups before failing. A failed --mode infer command can therefore leave the policy configuration and processors inconsistent. Valid JSON that is not an object has the same problem.

Parse all three checkpoint JSON objects before invoking any patch helpers. This keeps JSON input failures ahead of config writes and backup creation, while retaining the existing patching and backup behavior.

Six standard-library unittest methods run the actual CLI against temporary copies of the patcher, the two checked-in LLM configs and synthetic checkpoint JSON. They cover:

• Malformed and non-object JSON in each checkpoint file, in both export and infer modes, preserving the original bytes of all five config files.
• Missing required files.
• Successful updates, unrelated metadata, observation renaming, custom paths and action-step settings.
• Original-content backups, --no-backup and repeated mode switches.

Validation on Python 3.10.20 and 3.12.13: before the fix, eight input-preservation subtests fail; after it, all six test methods pass. Compilation and the PR diff whitespace check also pass.

python3 -m unittest discover -s tests -p test_prepare_model.py -v

This addresses checkpoint JSON parsing and object-type failures. File writes remain sequential; disk errors, concurrent edits, process interruption and invalid LLM Python configs are outside this change. The tests require no vendor SDK, model weights or robot hardware.

[839390315]

Hi @YeDonS, thank you for the contribution.

This PR currently has a merge conflict with the latest main branch in prepare_model.py, so GitHub is blocking the merge.
Once the updated commit is pushed and the tests pass, we will review and merge the PR. Thanks!