AgentDebugger-training-v3 / scratch /normalize_inputs.py
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import os
import sys
import pprint
sys.path.append(os.path.abspath('.'))
from data.generate_bugs import TIER1_BUGS, TIER2_BUGS, TIER3_BUGS
def normalize_test_cases(bugs):
for b in bugs:
for t in b.get("test_cases", []):
inp = t["input"]
if isinstance(inp, (list, tuple)):
t["input"] = list(inp)
else:
t["input"] = [inp]
normalize_test_cases(TIER1_BUGS)
normalize_test_cases(TIER2_BUGS)
normalize_test_cases(TIER3_BUGS)
def dump_var(f, name, val):
f.write(f'{name} = ')
f.write(pprint.pformat(val, sort_dicts=False, width=120))
f.write('\n\n')
with open("data/generate_bugs.py", "w", encoding="utf-8") as f:
f.write('"""\nAgentDebuggerEnv - Bug Dataset Generator\n\n')
f.write('Generates three tiers of buggy Python functions for curriculum learning:\n')
f.write(' Tier 1 (easy): Off-by-one errors, wrong operators, simple logic inversions\n')
f.write(' Tier 2 (medium): Incorrect algorithm logic, wrong variable references, subtle type errors\n')
f.write(' Tier 3 (hard): Multi-bug interactions, concurrency, edge-case-only failures\n\n')
f.write('Usage:\n python data/generate_bugs.py\n\n')
f.write('Outputs:\n data/bugs_tier1.jsonl (~40 bugs)\n data/bugs_tier2.jsonl (~30 bugs)\n data/bugs_tier3.jsonl (~20 bugs)\n"""\n\n')
f.write('import json\nimport os\n\n')
dump_var(f, 'TIER1_BUGS', TIER1_BUGS)
dump_var(f, 'TIER2_BUGS', TIER2_BUGS)
dump_var(f, 'TIER3_BUGS', TIER3_BUGS)
f.write('def write_jsonl(bugs: list, path: str):\n')
f.write(' with open(path, "w") as f:\n')
f.write(' for bug in bugs:\n')
f.write(' f.write(json.dumps(bug) + "\\n")\n\n')
f.write('if __name__ == "__main__":\n')
f.write(' os.makedirs("data", exist_ok=True)\n')
f.write(' write_jsonl(TIER1_BUGS, "data/bugs_tier1.jsonl")\n')
f.write(' write_jsonl(TIER2_BUGS, "data/bugs_tier2.jsonl")\n')
f.write(' write_jsonl(TIER3_BUGS, "data/bugs_tier3.jsonl")\n')
f.write(' print(f"Tier 1: {len(TIER1_BUGS)}, Tier 2: {len(TIER2_BUGS)}, Tier 3: {len(TIER3_BUGS)}")\n')
f.write(' print("\\nDone. Run training/train_grpo.py to start training.")\n')
print("Normalization applied successfully.")