"""Export only CUDA/NVTX aggregates; omit process arguments and environment."""
import sqlite3,json,sys,collections
from pathlib import Path

def merge(intervals):
 out=[]
 for a,b in sorted(intervals):
  if b<=a:continue
  if out and a<=out[-1][1]:out[-1]=(out[-1][0],max(out[-1][1],b))
  else:out.append((a,b))
 return out

def duration(xs):return sum(b-a for a,b in xs)/1e9

def overlap(a,b):
 i=j=0;total=0
 while i<len(a) and j<len(b):
  total+=max(0,min(a[i][1],b[j][1])-max(a[i][0],b[j][0]))
  if a[i][1]<b[j][1]:i+=1
  else:j+=1
 return total/1e9

for fn in sys.argv[1:]:
 db=sqlite3.connect(fn);db.row_factory=sqlite3.Row
 tables={r[0] for r in db.execute("select name from sqlite_master where type='table'")}
 strings=dict(db.execute('select id,value from StringIds'))
 gpu=[];kerns=[];api=collections.defaultdict(lambda:[0,0]);nv=collections.defaultdict(list);dispatch=collections.Counter();dispatch_timeline=[]
 for table in ['CUPTI_ACTIVITY_KIND_KERNEL','CUPTI_ACTIVITY_KIND_MEMCPY','CUPTI_ACTIVITY_KIND_MEMSET']:
  if table in tables:
   rows=list(db.execute('select * from '+table));gpu.extend((r['start'],r['end']) for r in rows)
   if table.endswith('KERNEL'):kerns=rows
 for table in ['CUPTI_ACTIVITY_KIND_RUNTIME','CUPTI_ACTIVITY_KIND_DRIVER']:
  if table in tables:
   for r in db.execute('select start,end,nameId from '+table):
    name=strings.get(r['nameId'],str(r['nameId']));api[name][0]+=1;api[name][1]+=(r['end']-r['start'])/1e9
 if 'NVTX_EVENTS' in tables:
  for r in db.execute('select * from NVTX_EVENTS'):
   keys=r.keys();name=r['text'] if 'text' in keys else None
   if not name and 'textId' in keys:name=strings.get(r['textId'],'')
   name=name or ''
   if name.startswith('CG_DISPATCH:'):
    dispatch[name]+=1;dispatch_timeline.append({'start_ns':r['start'],'name':name})
   if name.startswith('PLE_') and r['end'] is not None:nv[name].append((r['start'],r['end']))
 merged=merge(gpu);span=(merged[-1][1]-merged[0][0])/1e9 if merged else 0
 nvout={}
 for name,ranges in nv.items():
  u=merge(ranges);nvout[name]={'count':len(ranges),'sum_s':duration(ranges),'union_s':duration(u),'gpu_overlap_s':overlap(u,merged)}
 kernel_names=collections.defaultdict(lambda:[0,0])
 for r in kerns:
  name=strings.get(r['shortName'],'?');kernel_names[name][0]+=1;kernel_names[name][1]+=(r['end']-r['start'])/1e9
 result={'file':Path(fn).name,'gpu_activity_span_s':span,'gpu_busy_union_s':duration(merged),'gpu_idle_within_span_s':span-duration(merged),'kernel_count':len(kerns),'graph_node_kernel_count':sum(bool(r['graphNodeId']) for r in kerns),'api':dict(sorted(api.items(),key=lambda x:-x[1][1])),'nvtx':nvout,'dispatch':dict(dispatch),'dispatch_timeline':dispatch_timeline,'top_kernels':sorted(kernel_names.items(),key=lambda x:-x[1][1])[:20],'notes':'GPU busy is interval union, not utilization counter. CPU/API/NVTX times overlap GPU work and each other; do not sum them as exclusive costs.'}
 dest=Path(fn).with_suffix('.summary.json');dest.write_text(json.dumps(result,indent=2));print(dest)