[IMPROVEMENT] Normalize line endings and trailing whitespace in regression comparisons

[gaurav02081]

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This PR reduces false regression failures by normalizing non-semantic text differences during comparison.

CRLF and LF line endings are treated equivalently
Trailing whitespace at the end of lines does not cause diffs
Binary files are exempt from normalization

Changes

Updated TestResultFile.read_lines to normalize text output during comparison

Added a focused unit test covering:

CRLF vs LF normalization
Trailing whitespace handling
Binary file exemption

These differences are non-semantic and can introduce noise in regression results, making diffs harder to interpret.

Testing

Added unit tests (tests/test_normalization.py)

Tests pass locally

[canihavesomecoffee]

If we have a platform that wants to make sure that there's no changes at all to generated subtitle files, is it a good idea to start ignoring certain changes such as the ones you mention?

[sonarqubecloud]

Quality Gate passed

Issues
0 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud

[gaurav02081]

--> I went ahead and validated this locally to make sure the normalization doesn’t hide any real subtitle changes.

I tested the behavior using a small standalone script (verify_normalization.py) that directly exercises TestResultFile.read_lines(not commited this PR) in isolation. The script generates temporary subtitle files and compares them across several scenarios:

• Identical files

• CRLF vs LF line endings

• Trailing whitespace differences

• Actual subtitle text changes

• Timestamp (timing) changes

• Binary files (e.g. .jpg) to confirm normalization is skipped

The results show that only non-semantic differences (line endings and trailing whitespace) are normalized, while real content and timing changes are still detected correctly. Binary files are excluded from normalization as intended.

i am attaching the output image of the script .