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zeekay
bde2e2531b
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
47 lines
1.0 KiB
Python
47 lines
1.0 KiB
Python
# -*- coding: utf-8 -*-
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"""Helper script for github actions to combine job statuses"""
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import argparse
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import json
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RESULTS_TO_DISPLAY_LEVEL = {
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"failure": 0,
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"cancelled": 1,
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"success": 2,
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"skipped": 3,
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}
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DISPLAY_LEVEL_TO_RESULTS = {val: key for key, val in RESULTS_TO_DISPLAY_LEVEL.items()}
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def main(args):
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"""Entry point
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Args:
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args (List[str]): a list of arguments
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"""
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need_context_data = None
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with open(args.needs_context_json, encoding="utf-8") as f:
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need_context_data = json.load(f)
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display_level = min(
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RESULTS_TO_DISPLAY_LEVEL[job_object["result"]] for job_object in need_context_data.values()
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)
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print(DISPLAY_LEVEL_TO_RESULTS[display_level])
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if __name__ == "__main__":
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parser = argparse.ArgumentParser("Combine github actions statuses", allow_abbrev=False)
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parser.add_argument(
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"--needs_context_json",
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type=str,
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help="Pass the json file path containing the workflow needs context",
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)
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cli_args = parser.parse_args()
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main(cli_args)
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