17e3e23f06
- Add _cache_analysis_tracks() to cache each analysis track individually with content-hash IDs, replacing inline data with cache-id refs - Add _resolve_analysis_refs() to resolve cache-id refs back to full data - Add extract_segment_with_loop() helper that detects when output is shorter than requested duration and re-runs with -stream_loop -1 - Update COMPOUND handler's FFmpeg and Python paths to use looping - This fixes videos shorter than audio duration being truncated Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
529 lines
19 KiB
Python
529 lines
19 KiB
Python
#!/usr/bin/env python3
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"""
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Run a staged recipe through analyze -> plan -> execute pipeline.
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This script demonstrates stage-level caching: analysis stages can be
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skipped on re-run if the inputs haven't changed.
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Usage:
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python3 run_staged.py recipe.sexp [-o output.mp4]
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python3 run_staged.py effects/ascii_art_staged.sexp -o ascii_out.mp4
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The script:
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1. Compiles the recipe and extracts stage information
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2. For each stage in topological order:
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- Check stage cache (skip if hit)
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- Run stage (analyze, plan, execute)
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- Cache stage outputs
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3. Produce final output
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"""
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import os
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import sys
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import json
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import tempfile
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import shutil
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import subprocess
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from pathlib import Path
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from typing import Dict, List, Optional, Any
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# Add artdag to path
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sys.path.insert(0, str(Path(__file__).parent.parent / "artdag"))
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from artdag.sexp import compile_string, parse
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from artdag.sexp.parser import Symbol, Keyword, serialize
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from artdag.sexp.planner import create_plan
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# Import unified cache
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import cache as unified_cache
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import hashlib
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def _cache_analysis_tracks(plan):
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"""Cache each analysis track individually, replace data with cache-id refs."""
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import json as _json
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for name, data in plan.analysis.items():
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json_str = _json.dumps(data, sort_keys=True)
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content_cid = hashlib.sha256(json_str.encode()).hexdigest()
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unified_cache.cache_store_json(content_cid, data)
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plan.analysis[name] = {"_cache_id": content_cid}
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def _resolve_analysis_refs(analysis_dict):
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"""Resolve cache-id refs back to full analysis data."""
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resolved = {}
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for name, data in analysis_dict.items():
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if isinstance(data, dict) and "_cache_id" in data:
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loaded = unified_cache.cache_get_json(data["_cache_id"])
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if loaded:
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resolved[name] = loaded
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else:
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resolved[name] = data
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return resolved
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def run_staged_recipe(
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recipe_path: Path,
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output_path: Optional[Path] = None,
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cache_dir: Optional[Path] = None,
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params: Optional[Dict[str, Any]] = None,
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verbose: bool = True,
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force_replan: bool = False,
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) -> Path:
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"""
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Run a staged recipe with stage-level caching.
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Args:
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recipe_path: Path to the .sexp recipe file
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output_path: Optional output file path
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cache_dir: Optional cache directory for stage results
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params: Optional parameter overrides
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verbose: Print progress information
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Returns:
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Path to the final output file
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"""
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recipe_text = recipe_path.read_text()
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recipe_dir = recipe_path.parent
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# Use unified cache
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content_cache_dir = unified_cache.get_content_dir()
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def log(msg: str):
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if verbose:
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print(msg, file=sys.stderr)
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# Store recipe source by CID
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recipe_cid, _ = unified_cache.content_store_string(recipe_text)
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log(f"Recipe CID: {recipe_cid[:16]}...")
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# Compile recipe
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log(f"Compiling: {recipe_path}")
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compiled = compile_string(recipe_text, params, recipe_dir=recipe_dir)
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log(f"Recipe: {compiled.name} v{compiled.version}")
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log(f"Nodes: {len(compiled.nodes)}")
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# Store effects by CID
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for effect_name, effect_info in compiled.registry.get("effects", {}).items():
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effect_path = effect_info.get("path")
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effect_cid = effect_info.get("cid")
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if effect_path and effect_cid:
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effect_file = Path(effect_path)
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if effect_file.exists():
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stored_cid, _ = unified_cache.content_store_file(effect_file)
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if stored_cid == effect_cid:
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log(f"Effect '{effect_name}' CID: {effect_cid[:16]}...")
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else:
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log(f"Warning: Effect '{effect_name}' CID mismatch")
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# Store analyzers by CID
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for analyzer_name, analyzer_info in compiled.registry.get("analyzers", {}).items():
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analyzer_path = analyzer_info.get("path")
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analyzer_cid = analyzer_info.get("cid")
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if analyzer_path:
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analyzer_file = Path(analyzer_path) if Path(analyzer_path).is_absolute() else recipe_dir / analyzer_path
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if analyzer_file.exists():
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stored_cid, _ = unified_cache.content_store_file(analyzer_file)
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log(f"Analyzer '{analyzer_name}' CID: {stored_cid[:16]}...")
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# Store included files by CID
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for include_path, include_cid in compiled.registry.get("includes", {}).items():
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include_file = Path(include_path)
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if include_file.exists():
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stored_cid, _ = unified_cache.content_store_file(include_file)
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if stored_cid == include_cid:
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log(f"Include '{include_file.name}' CID: {include_cid[:16]}...")
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else:
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log(f"Warning: Include '{include_file.name}' CID mismatch")
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# Check for stages
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if not compiled.stages:
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log("No stages found - running as regular recipe")
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return _run_non_staged(compiled, recipe_dir, output_path, verbose)
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log(f"\nStages: {len(compiled.stages)}")
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log(f"Stage order: {compiled.stage_order}")
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# Display stage info
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for stage in compiled.stages:
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log(f"\n Stage: {stage.name}")
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log(f" Requires: {stage.requires or '(none)'}")
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log(f" Inputs: {stage.inputs or '(none)'}")
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log(f" Outputs: {stage.outputs}")
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# Create plan with analysis
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log("\n--- Planning ---")
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analysis_data = {}
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def on_analysis(node_id: str, results: dict):
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analysis_data[node_id] = results
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times = results.get("times", [])
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log(f" Analysis complete: {node_id[:16]}... ({len(times)} times)")
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# Check for cached plan using unified cache
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plan_cid = unified_cache.plan_exists(recipe_cid, params)
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if plan_cid and not force_replan:
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plan_cache_path = unified_cache.plan_get_path(recipe_cid, params)
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log(f"\nFound cached plan: {plan_cid[:16]}...")
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plan_sexp_str = unified_cache.plan_load(recipe_cid, params)
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# Parse the cached plan
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from execute import parse_plan_input
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plan_dict = parse_plan_input(plan_sexp_str)
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# Resolve cache-id refs in plan's embedded analysis
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if "analysis" in plan_dict:
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plan_dict["analysis"] = _resolve_analysis_refs(plan_dict["analysis"])
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# Load analysis data from unified cache
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analysis_data = {}
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for step in plan_dict.get("steps", []):
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if step.get("node_type") == "ANALYZE":
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step_id = step.get("step_id")
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cached_analysis = unified_cache.cache_get_json(step_id)
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if cached_analysis:
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analysis_data[step_id] = cached_analysis
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log(f" Loaded analysis: {step_id[:16]}...")
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log(f"Plan ID: {plan_dict.get('plan_id', 'unknown')[:16]}...")
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log(f"Steps: {len(plan_dict.get('steps', []))}")
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log(f"Analysis tracks: {list(analysis_data.keys())}")
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# Execute directly from cached plan
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log("\n--- Execution (from cached plan) ---")
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from execute import execute_plan
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result_path = execute_plan(
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plan_path=plan_cache_path,
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output_path=output_path,
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recipe_dir=recipe_dir,
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external_analysis=analysis_data,
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cache_dir=content_cache_dir,
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)
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log(f"\n--- Complete ---")
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log(f"Output: {result_path}")
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return result_path
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# No cached plan - create new one
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plan = create_plan(
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compiled,
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inputs={},
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recipe_dir=recipe_dir,
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on_analysis=on_analysis,
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)
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log(f"\nPlan ID: {plan.plan_id[:16]}...")
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log(f"Steps: {len(plan.steps)}")
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log(f"Analysis tracks: {list(analysis_data.keys())}")
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# Cache analysis tracks individually and replace with cache-id refs
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_cache_analysis_tracks(plan)
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# Save plan to unified cache
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plan_sexp_str = plan.to_string(pretty=True)
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plan_cache_id, plan_cid, plan_cache_path = unified_cache.plan_store(recipe_cid, params, plan_sexp_str)
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log(f"Saved plan: {plan_cache_id[:16]}... → {plan_cid[:16]}...")
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# Execute the plan using execute.py logic
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log("\n--- Execution ---")
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from execute import execute_plan
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# Resolve cache-id refs back to full data for execution
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resolved_analysis = _resolve_analysis_refs(plan.analysis)
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plan_dict = {
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"plan_id": plan.plan_id,
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"source_hash": plan.source_hash,
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"encoding": compiled.encoding,
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"output_step_id": plan.output_step_id,
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"analysis": {**resolved_analysis, **analysis_data},
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"effects_registry": plan.effects_registry,
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"minimal_primitives": plan.minimal_primitives,
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"steps": [],
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}
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for step in plan.steps:
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step_dict = {
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"step_id": step.step_id,
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"node_type": step.node_type,
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"config": step.config,
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"inputs": step.inputs,
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"level": step.level,
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"cache_id": step.cache_id,
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}
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# Tag with stage info if present
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if step.stage:
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step_dict["stage"] = step.stage
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plan_dict["steps"].append(step_dict)
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# Execute using unified cache
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result_path = execute_plan(
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plan_path=None,
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output_path=output_path,
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recipe_dir=recipe_dir,
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plan_data=plan_dict,
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external_analysis=analysis_data,
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cache_dir=content_cache_dir,
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)
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log(f"\n--- Complete ---")
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log(f"Output: {result_path}")
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return result_path
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def _run_non_staged(compiled, recipe_dir: Path, output_path: Optional[Path], verbose: bool) -> Path:
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"""Run a non-staged recipe using the standard pipeline."""
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from execute import execute_plan
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from plan import plan_recipe
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# This is a fallback for recipes without stages
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# Just run through regular plan -> execute
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raise NotImplementedError("Non-staged recipes should use plan.py | execute.py")
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def list_cache(verbose: bool = False):
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"""List all cached items using the unified cache."""
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unified_cache.print_cache_listing(verbose)
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def list_params(recipe_path: Path):
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"""List available parameters for a recipe and its effects."""
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from artdag.sexp import parse
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from artdag.sexp.parser import Symbol, Keyword
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from artdag.sexp.compiler import _parse_params
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from artdag.sexp.effect_loader import load_sexp_effect_file
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recipe_text = recipe_path.read_text()
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sexp = parse(recipe_text)
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if isinstance(sexp, list) and len(sexp) == 1:
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sexp = sexp[0]
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# Find recipe name
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recipe_name = sexp[1] if len(sexp) > 1 and isinstance(sexp[1], str) else recipe_path.stem
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# Find :params block and effect declarations
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recipe_params = []
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effect_declarations = {} # name -> path
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i = 2
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while i < len(sexp):
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item = sexp[i]
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if isinstance(item, Keyword) and item.name == "params":
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if i + 1 < len(sexp):
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recipe_params = _parse_params(sexp[i + 1])
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i += 2
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elif isinstance(item, list) and item:
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# Check for effect declaration: (effect name :path "...")
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if isinstance(item[0], Symbol) and item[0].name == "effect":
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if len(item) >= 2:
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effect_name = item[1].name if isinstance(item[1], Symbol) else item[1]
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# Find :path
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j = 2
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while j < len(item):
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if isinstance(item[j], Keyword) and item[j].name == "path":
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if j + 1 < len(item):
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effect_declarations[effect_name] = item[j + 1]
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break
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j += 1
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i += 1
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else:
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i += 1
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# Load effect params
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effect_params = {} # effect_name -> list of ParamDef
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recipe_dir = recipe_path.parent
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for effect_name, effect_rel_path in effect_declarations.items():
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effect_path = recipe_dir / effect_rel_path
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if effect_path.exists() and effect_path.suffix == ".sexp":
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try:
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_, _, _, param_defs = load_sexp_effect_file(effect_path)
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if param_defs:
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effect_params[effect_name] = param_defs
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except Exception as e:
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print(f"Warning: Could not load params from effect {effect_name}: {e}", file=sys.stderr)
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# Print results
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def print_params(params, header_prefix=""):
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print(f"{header_prefix}{'Name':<20} {'Type':<8} {'Default':<12} {'Range/Choices':<20} Description")
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print(f"{header_prefix}{'-' * 88}")
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for p in params:
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range_str = ""
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if p.range_min is not None and p.range_max is not None:
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range_str = f"[{p.range_min}, {p.range_max}]"
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elif p.choices:
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range_str = ", ".join(p.choices[:3])
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if len(p.choices) > 3:
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range_str += "..."
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default_str = str(p.default) if p.default is not None else "-"
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if len(default_str) > 10:
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default_str = default_str[:9] + "…"
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print(f"{header_prefix}{p.name:<20} {p.param_type:<8} {default_str:<12} {range_str:<20} {p.description}")
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if recipe_params:
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print(f"\nRecipe parameters for '{recipe_name}':\n")
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print_params(recipe_params)
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else:
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print(f"\nRecipe '{recipe_name}' has no declared parameters.")
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if effect_params:
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for effect_name, params in effect_params.items():
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print(f"\n\nEffect '{effect_name}' parameters:\n")
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print_params(params)
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if not recipe_params and not effect_params:
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print("\nParameters can be declared using :params block:")
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print("""
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:params (
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(color_mode :type string :default "color" :desc "Character color")
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(char_size :type int :default 12 :range [4 32] :desc "Cell size")
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)
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""")
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return
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print("\n\nUsage:")
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print(f" python3 run_staged.py {recipe_path} -p <name>=<value> [-p <name>=<value> ...]")
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print(f"\nExample:")
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all_params = recipe_params + [p for params in effect_params.values() for p in params]
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if all_params:
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p = all_params[0]
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example_val = p.default if p.default else ("value" if p.param_type == "string" else "1")
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print(f" python3 run_staged.py {recipe_path} -p {p.name}={example_val}")
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def main():
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import argparse
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parser = argparse.ArgumentParser(
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description="Run a staged recipe with stage-level caching",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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python3 run_staged.py effects/ascii_art_fx_staged.sexp --list-params
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python3 run_staged.py effects/ascii_art_fx_staged.sexp -o output.mp4
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python3 run_staged.py recipe.sexp -p color_mode=lime -p char_jitter=5
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"""
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)
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parser.add_argument("recipe", type=Path, nargs="?", help="Recipe file (.sexp)")
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parser.add_argument("-o", "--output", type=Path, help="Output file path")
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parser.add_argument("-p", "--param", action="append", dest="params",
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metavar="KEY=VALUE", help="Set recipe parameter")
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parser.add_argument("-q", "--quiet", action="store_true", help="Suppress progress output")
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parser.add_argument("--list-params", action="store_true", help="List available parameters and exit")
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parser.add_argument("--list-cache", action="store_true", help="List cached items and exit")
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parser.add_argument("--no-cache", action="store_true", help="Ignore cached plan, force re-planning")
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parser.add_argument("--show-plan", action="store_true", help="Show the plan S-expression and exit (don't execute)")
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parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
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parser.add_argument("-j", "--jobs", type=int, default=None,
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help="Max parallel workers (default: 4, or ARTDAG_WORKERS env)")
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parser.add_argument("--pipelines", type=int, default=None,
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help="Max concurrent video pipelines (default: 1, or ARTDAG_VIDEO_PIPELINES env)")
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args = parser.parse_args()
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# Apply concurrency limits before any execution
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if args.jobs is not None:
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os.environ["ARTDAG_WORKERS"] = str(args.jobs)
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if args.pipelines is not None:
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os.environ["ARTDAG_VIDEO_PIPELINES"] = str(args.pipelines)
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from execute import set_max_video_pipelines
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set_max_video_pipelines(args.pipelines)
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# List cache mode - doesn't require recipe
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if args.list_cache:
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list_cache(verbose=args.verbose)
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sys.exit(0)
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# All other modes require a recipe
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if not args.recipe:
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print("Error: recipe file required", file=sys.stderr)
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sys.exit(1)
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if not args.recipe.exists():
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print(f"Recipe not found: {args.recipe}", file=sys.stderr)
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sys.exit(1)
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# List params mode
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if args.list_params:
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list_params(args.recipe)
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sys.exit(0)
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# Parse parameters
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params = {}
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if args.params:
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for param_str in args.params:
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if "=" not in param_str:
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print(f"Invalid parameter format: {param_str}", file=sys.stderr)
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sys.exit(1)
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key, value = param_str.split("=", 1)
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# Try to parse as number
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try:
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|
value = int(value)
|
|
except ValueError:
|
|
try:
|
|
value = float(value)
|
|
except ValueError:
|
|
pass # Keep as string
|
|
params[key] = value
|
|
|
|
# Show plan mode - generate plan and display without executing
|
|
if args.show_plan:
|
|
recipe_text = args.recipe.read_text()
|
|
recipe_dir = args.recipe.parent
|
|
|
|
# Compute recipe CID (content hash)
|
|
recipe_cid, _ = unified_cache.content_store_string(recipe_text)
|
|
|
|
compiled = compile_string(recipe_text, params if params else None, recipe_dir=recipe_dir)
|
|
|
|
# Check for cached plan using unified cache (keyed by source CID + params)
|
|
plan_cid = unified_cache.plan_exists(recipe_cid, params if params else None)
|
|
|
|
if plan_cid and not args.no_cache:
|
|
print(f";; Cached plan CID: {plan_cid}", file=sys.stderr)
|
|
plan_sexp_str = unified_cache.plan_load(recipe_cid, params if params else None)
|
|
print(plan_sexp_str)
|
|
else:
|
|
print(f";; Generating new plan...", file=sys.stderr)
|
|
analysis_data = {}
|
|
def on_analysis(node_id: str, results: dict):
|
|
analysis_data[node_id] = results
|
|
|
|
plan = create_plan(
|
|
compiled,
|
|
inputs={},
|
|
recipe_dir=recipe_dir,
|
|
on_analysis=on_analysis,
|
|
)
|
|
# Cache analysis tracks individually before serialization
|
|
_cache_analysis_tracks(plan)
|
|
plan_sexp_str = plan.to_string(pretty=True)
|
|
|
|
# Save to unified cache
|
|
cache_id, plan_cid, plan_path = unified_cache.plan_store(recipe_cid, params if params else None, plan_sexp_str)
|
|
print(f";; Saved: {cache_id[:16]}... → {plan_cid}", file=sys.stderr)
|
|
print(plan_sexp_str)
|
|
sys.exit(0)
|
|
|
|
result = run_staged_recipe(
|
|
recipe_path=args.recipe,
|
|
output_path=args.output,
|
|
params=params if params else None,
|
|
verbose=not args.quiet,
|
|
force_replan=args.no_cache,
|
|
)
|
|
|
|
# Print final output path
|
|
print(result)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|