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fix/626-va
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queue/375-
| Author | SHA1 | Date | |
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| 8cf741115a |
361
deploy-crons.py
361
deploy-crons.py
@@ -1,153 +1,292 @@
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#!/usr/bin/env python3
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"""
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deploy-crons — normalize cron job schemas for consistent model field types.
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deploy-crons -- deploy cron jobs from YAML config and normalize jobs.json.
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This script ensures that the model field in jobs.json is always a dict when
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either model or provider is specified, preventing schema inconsistency.
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Two modes:
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--deploy Sync jobs from cron-jobs.yaml into jobs.json (create / update).
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--normalize Normalize model field types in existing jobs.json.
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The --deploy comparison checks prompt, schedule, model, and provider so
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that model/provider-only changes are never silently dropped.
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Usage:
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python deploy-crons.py [--dry-run] [--jobs-file PATH]
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python deploy-crons.py --deploy [--config PATH] [--jobs-file PATH] [--dry-run]
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python deploy-crons.py --normalize [--jobs-file PATH] [--dry-run]
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"""
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import argparse
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import json
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import sys
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import uuid
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from pathlib import Path
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from typing import Any, Dict, Optional
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from typing import Any, Dict, List, Optional
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try:
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import yaml
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HAS_YAML = True
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except ImportError:
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HAS_YAML = False
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _flat_model(job: Dict[str, Any]) -> Optional[str]:
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"""Extract flat model string from dict or string model field."""
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m = job.get("model")
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if isinstance(m, dict):
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return m.get("model")
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return m
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def _flat_provider(job: Dict[str, Any]) -> Optional[str]:
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"""Extract flat provider string from dict model field or top-level."""
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m = job.get("model")
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if isinstance(m, dict):
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return m.get("provider")
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return job.get("provider")
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def normalize_job(job: Dict[str, Any]) -> Dict[str, Any]:
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"""
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Normalize a job dict to ensure consistent model field types.
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Before normalization:
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- If model AND provider: model = raw string, provider = raw string (inconsistent)
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- If only model: model = raw string
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- If only provider: provider = raw string at top level
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After normalization:
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- If model exists: model = {"model": "xxx"}
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- If provider exists: model = {"provider": "yyy"}
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- If both exist: model = {"model": "xxx", "provider": "yyy"}
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- If neither: model = None
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"""
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job = dict(job) # Create a copy to avoid modifying the original
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"""Normalize a job dict to ensure consistent model field types."""
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job = dict(job)
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model = job.get("model")
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provider = job.get("provider")
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# Skip if already normalized (model is a dict)
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if isinstance(model, dict):
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return job
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# Build normalized model dict
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model_dict = {}
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if model is not None and isinstance(model, str):
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model_dict["model"] = model.strip()
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if provider is not None and isinstance(provider, str):
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model_dict["provider"] = provider.strip()
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# Set model field
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if model_dict:
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job["model"] = model_dict
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else:
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job["model"] = None
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# Remove top-level provider field if it was moved into model dict
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if provider is not None and "provider" in model_dict:
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# Keep provider field for backward compatibility but mark it as deprecated
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# This allows existing code that reads job["provider"] to continue working
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pass
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job["model"] = model_dict if model_dict else None
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return job
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def normalize_jobs_file(jobs_file: Path, dry_run: bool = False) -> int:
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# ---------------------------------------------------------------------------
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# Deploy from YAML
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# ---------------------------------------------------------------------------
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def _jobs_changed(cur: Dict[str, Any], desired: Dict[str, Any]) -> bool:
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"""
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Normalize all jobs in a jobs.json file.
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Returns the number of jobs that were modified.
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Return True if desired differs from cur.
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Compares prompt, schedule, model, and provider -- the fix for #375.
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Previously only prompt and schedule were compared, silently dropping
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model/provider changes when the prompt was unchanged.
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"""
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if not jobs_file.exists():
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print(f"Error: Jobs file not found: {jobs_file}", file=sys.stderr)
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return 1
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if cur.get("prompt") != desired.get("prompt"):
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return True
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if cur.get("schedule") != desired.get("schedule"):
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return True
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if _flat_model(cur) != _flat_model(desired):
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return True
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if _flat_provider(cur) != _flat_provider(desired):
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return True
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return False
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def _parse_schedule(schedule: str) -> Dict[str, Any]:
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"""Parse schedule string into structured format."""
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try:
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with open(jobs_file, 'r', encoding='utf-8') as f:
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data = json.load(f)
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except json.JSONDecodeError as e:
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print(f"Error: Invalid JSON in {jobs_file}: {e}", file=sys.stderr)
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from cron.jobs import parse_schedule
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return parse_schedule(schedule)
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except ImportError:
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pass
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schedule = schedule.strip()
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if schedule.startswith("every "):
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dur = schedule[6:].strip()
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unit = dur[-1]
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val = int(dur[:-1])
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minutes = val * {"m": 1, "h": 60, "d": 1440}.get(unit, 1)
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return {"kind": "interval", "minutes": minutes, "display": f"every {minutes}m"}
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return {"kind": "cron", "expr": schedule, "display": schedule}
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def deploy_from_yaml(
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config_path: Path,
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jobs_file: Path,
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dry_run: bool = False,
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) -> int:
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"""Sync jobs from YAML config into jobs.json."""
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if not HAS_YAML:
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print("Error: PyYAML required for --deploy. pip install pyyaml", file=sys.stderr)
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return 1
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if not config_path.exists():
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print(f"Error: Config not found: {config_path}", file=sys.stderr)
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return 1
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with open(config_path, "r", encoding="utf-8") as f:
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yaml_jobs = (yaml.safe_load(f) or {}).get("jobs", [])
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if jobs_file.exists():
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with open(jobs_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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else:
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data = {"jobs": [], "updated_at": None}
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existing: List[Dict[str, Any]] = data.get("jobs", [])
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# Index existing jobs by prompt+schedule for matching
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index: Dict[str, int] = {}
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for i, j in enumerate(existing):
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key = f"{j.get('prompt', '')}||{json.dumps(j.get('schedule', {}), sort_keys=True)}"
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index[key] = i
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created = updated = skipped = 0
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for spec in yaml_jobs:
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prompt = spec.get("prompt", "")
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schedule_str = spec.get("schedule", "")
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name = spec.get("name", "")
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model = spec.get("model")
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provider = spec.get("provider")
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skills = spec.get("skills", [])
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parsed_schedule = _parse_schedule(schedule_str)
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key = f"{prompt}||{json.dumps(parsed_schedule, sort_keys=True)}"
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desired = {
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"prompt": prompt,
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"schedule": parsed_schedule,
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"schedule_display": parsed_schedule.get("display", schedule_str),
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"model": model,
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"provider": provider,
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"skills": skills if isinstance(skills, list) else [skills] if skills else [],
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"name": name or prompt[:50].strip(),
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}
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if key in index:
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idx = index[key]
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cur = existing[idx]
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if _jobs_changed(cur, desired):
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if dry_run:
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print(f" WOULD UPDATE: {cur.get('id', '?')} ({cur.get('name', '?')})")
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print(f" model: {_flat_model(cur)!r} -> {model!r}")
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print(f" provider: {_flat_provider(cur)!r} -> {provider!r}")
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else:
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existing[idx].update(desired)
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updated += 1
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else:
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skipped += 1
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else:
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if dry_run:
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print(f" WOULD CREATE: ({name or prompt[:50]})")
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else:
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job_id = uuid.uuid4().hex[:12]
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new_job = {
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"id": job_id,
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"enabled": True,
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"state": "scheduled",
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"paused_at": None,
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"paused_reason": None,
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"created_at": None,
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"next_run_at": None,
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"last_run_at": None,
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"last_status": None,
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"last_error": None,
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"repeat": {"times": None, "completed": 0},
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"deliver": "local",
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"origin": None,
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"base_url": None,
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"script": None,
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**desired,
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}
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existing.append(new_job)
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created += 1
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if dry_run:
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print(f"DRY RUN: {created} to create, {updated} to update, {skipped} unchanged.")
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return 0
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data["jobs"] = existing
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jobs_file.parent.mkdir(parents=True, exist_ok=True)
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with open(jobs_file, "w", encoding="utf-8") as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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print(f"Deployed: {created} created, {updated} updated, {skipped} unchanged.")
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return 0
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# ---------------------------------------------------------------------------
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# Normalize standalone
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# ---------------------------------------------------------------------------
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def normalize_jobs_file(jobs_file: Path, dry_run: bool = False) -> int:
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"""Normalize model field types in jobs.json."""
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if not jobs_file.exists():
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print(f"Error: {jobs_file}", file=sys.stderr)
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return 1
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with open(jobs_file, "r", encoding="utf-8") as f:
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data = json.load(f)
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jobs = data.get("jobs", [])
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if not jobs:
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print("No jobs found in file.")
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print("No jobs found.")
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return 0
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modified_count = 0
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for i, job in enumerate(jobs):
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original_model = job.get("model")
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original_provider = job.get("provider")
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normalized_job = normalize_job(job)
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# Check if anything changed
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if (normalized_job.get("model") != original_model or
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normalized_job.get("provider") != original_provider):
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jobs[i] = normalized_job
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modified_count += 1
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job_id = job.get("id", "?")
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job_name = job.get("name", "(unnamed)")
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print(f"Normalized job {job_id} ({job_name}):")
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print(f" model: {original_model!r} -> {normalized_job.get('model')!r}")
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print(f" provider: {original_provider!r} -> {normalized_job.get('provider')!r}")
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if modified_count == 0:
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print("All jobs already have consistent model field types.")
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return 0
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if dry_run:
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print(f"DRY RUN: Would normalize {modified_count} jobs.")
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return 0
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# Write back to file
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data["jobs"] = jobs
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try:
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with open(jobs_file, 'w', encoding='utf-8') as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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print(f"Normalized {modified_count} jobs in {jobs_file}")
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return 0
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except Exception as e:
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print(f"Error writing to {jobs_file}: {e}", file=sys.stderr)
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return 1
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modified = 0
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for i, job in enumerate(jobs):
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orig_model = job.get("model")
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orig_provider = job.get("provider")
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normed = normalize_job(job)
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if normed.get("model") != orig_model or normed.get("provider") != orig_provider:
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jobs[i] = normed
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modified += 1
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print(f"Normalized {job.get('id', '?')} ({job.get('name', '?')}):")
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print(f" model: {orig_model!r} -> {normed.get('model')!r}")
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print(f" provider: {orig_provider!r} -> {normed.get('provider')!r}")
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if modified == 0:
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print("All jobs already consistent.")
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return 0
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if dry_run:
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print(f"DRY RUN: Would normalize {modified} jobs.")
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return 0
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data["jobs"] = jobs
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with open(jobs_file, "w", encoding="utf-8") as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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print(f"Normalized {modified} jobs in {jobs_file}")
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return 0
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def main():
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parser = argparse.ArgumentParser(
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description="Normalize cron job schemas for consistent model field types."
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)
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parser.add_argument(
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"--dry-run",
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action="store_true",
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help="Show what would be changed without modifying the file."
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)
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parser.add_argument(
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"--jobs-file",
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type=Path,
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default=Path.home() / ".hermes" / "cron" / "jobs.json",
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help="Path to jobs.json file (default: ~/.hermes/cron/jobs.json)"
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)
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parser = argparse.ArgumentParser(description="Deploy and normalize cron jobs.")
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group = parser.add_mutually_exclusive_group(required=True)
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group.add_argument("--deploy", action="store_true",
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help="Sync jobs from YAML config to jobs.json")
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group.add_argument("--normalize", action="store_true",
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help="Normalize model field types in jobs.json")
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parser.add_argument("--config", type=Path,
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default=Path.home() / ".hermes" / "cron-jobs.yaml",
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help="Path to cron-jobs.yaml")
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parser.add_argument("--jobs-file", type=Path,
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default=Path.home() / ".hermes" / "cron" / "jobs.json",
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help="Path to jobs.json")
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parser.add_argument("--dry-run", action="store_true",
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help="Show changes without modifying files")
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args = parser.parse_args()
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if args.dry_run:
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print("DRY RUN MODE — no changes will be made.")
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print("DRY RUN MODE.")
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print()
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return normalize_jobs_file(args.jobs_file, args.dry_run)
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if args.deploy:
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return deploy_from_yaml(args.config, args.jobs_file, args.dry_run)
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else:
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return normalize_jobs_file(args.jobs_file, args.dry_run)
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if __name__ == "__main__":
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Reference in New Issue
Block a user