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Machine Learning with FHE - fork of Zama Concrete-ML - Built-in FHE-friendly ML models - scikit-learn compatible API - PyTorch model conversion - Quantization-aware training
60 lines
1.5 KiB
Python
60 lines
1.5 KiB
Python
""" Check an actionlint log against some whitelists """
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import sys
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from typing import Set
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# Exact lines which are whitelisted
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whitelisted_lines: Set[str] = set()
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# Pattern which are whitelisted
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whitelisted_pattern: Set[str] = {
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"matrix.runs_on",
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"matrix.python_version",
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"",
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"matrix: ${{ fromJSON(format('{{\"include\":{0}}}', "
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"needs.start-runner-linux.outputs.matrix)) }}",
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"matrix: ${{ fromJSON(format('{{\"include\":{0}}}', "
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"needs.matrix-preparation.outputs.macos-matrix)) }}",
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'when a reusable workflow is called with "uses", "strategy" is not available.'
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' only following keys are allowed: "name", "uses", "with", "secrets", "needs",'
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' "if", and "permissions" in job "run-job"',
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'"secrets" section is scalar node but mapping node is expected',
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"secrets: inherit",
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"^~~~~~~",
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"|",
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"strategy:",
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}
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def main():
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"""Do the test
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Raises:
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ValueError: if non whitelisted error occurred
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"""
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status = 0
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bad_lines = []
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for line in sys.stdin:
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if line in whitelisted_lines:
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continue
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is_bad_line = True
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for pattern in whitelisted_pattern:
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if pattern in line:
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is_bad_line = False
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break
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if is_bad_line:
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print("->", line)
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status = 1
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bad_lines.append(line)
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if status:
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errors = "\n------\n".join(bad_lines)
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raise ValueError("Some non whitelisted errors, look at full log file:" f"{errors}")
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if __name__ == "__main__":
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main()
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