fix: handle None test_ds in skipped messages count

[RuixiangMa]

Motivation

The problem occurs when test_ds is None due to split-eval being False by default

Modifications

Explicitly handles the case when test_ds is None

Related Issues

Accuracy Test

Benchmark & Profiling

Checklist

[gemini-code-assist]

Summary of Changes

Hello @RuixiangMa, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request addresses a runtime error that occurs when the test_ds (test dataset) variable is None, specifically when split-eval is disabled by default. It introduces a modification to the prepare_data.py script to safely calculate the total number of messages, ensuring the application continues to function correctly under these conditions and avoids crashes related to None type operations.

Highlights

• Error Handling: Correctly calculates the total number of messages when test_ds is None to prevent a TypeError in the skipped messages count print statement.

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[gemini-code-assist]

Code Review

This pull request correctly fixes a TypeError that occurs when test_ds is None by adding a conditional check. The fix is effective. I've added one suggestion to improve the clarity of the new variable name and the associated log message to avoid potential confusion.

[gemini-code-assist]

While this change correctly fixes the bug, the variable name total_messages is a bit misleading as it represents the total number of conversations (or dataset items), not the total number of messages within them. The resulting log message Skipped {X}/{Y} messages is ambiguous because it compares a count of skipped messages (X) to a count of conversations (Y).

To improve clarity and maintainability, I suggest renaming the variable to be more specific and updating the log message to reflect what is being counted.

Suggested change

	total_messages = len(train_ds) + (len(test_ds) if test_ds is not None else 0)
	print(
	f"Skipped {total_skipped_count}/{len(train_ds)+len(test_ds)} messages for {dataset_name}"
	f"Skipped {total_skipped_count}/{total_messages} messages for {dataset_name}"
	)
	total_conversations = len(train_ds) + (len(test_ds) if test_ds is not None else 0)
	print(
	f"Skipped {total_skipped_count} messages across {total_conversations} conversations for {dataset_name}"
	)