text: import os
  import random
  import time
  import torch
  import torch.nn as nn
  
  from itertools import accumulate
  from math import ceil
  
  from colbert.utils.runs import Run
  from colbert.utils.utils import print_message
  
  from colbert.evaluation.metrics import Metrics
  from colbert.evaluation.ranking_logger import RankingLogger
  from colbert.modeling.inference import ModelInference
  
  from colbert.evaluation.slow import slow_rerank
  
  
  def evaluate(args):
      args.inference = ModelInference(args.colbert, amp=args.amp)
      qrels, queries, topK_pids = args.qrels, args.queries, args.topK_pids
  
      depth = args.depth
      collection = args.collection
      if collection is None:
          topK_docs = args.topK_docs
  
      def qid2passages(qid):
          if collection is not None:
              return [collection[pid] for pid in topK_pids[qid][:depth]]
          else:
              return topK_docs[qid][:depth]
  
      metrics = Metrics(mrr_depths={10, 100}, recall_depths={50, 200, 1000},
                        success_depths={5, 10, 20, 50, 100, 1000},
                        total_queries=len(queries))
  
      ranking_logger = RankingLogger(Run.path, qrels=qrels)
  
      args.milliseconds = []
  
      with ranking_logger.context('ranking.tsv', also_save_annotations=(qrels is not None)) as rlogger:
          with torch.no_grad():
              keys = sorted(list(queries.keys()))
              random.shuffle(keys)
  
              for query_idx, qid in enumerate(keys):
                  query = queries[qid]
  
                  print_message(query_idx, qid, query, '\n')
  
                  if qrels and args.shortcircuit and len(set.intersection(set(qrels[qid]), set(topK_pids[qid]))) == 0:
                      continue
  
                  ranking = slow_rerank(args, query, topK_pids[qid], qid2passages(qid))
  
                  rlogger.log(qid, ranking, [0, 1])
  
                  if qrels:
                      metrics.add(query_idx, qid, ranking, qrels[qid])
  
                      for i, (score, pid, passage) in enumerate(ranking):
                          if pid in qrels[qid]:
                              print("\n#> Found", pid, "at position", i+1, "with score", score)
                              print(passage)
                              break
  
                      metrics.print_metrics(query_idx)
                      metrics.log(query_idx)
  
                  print_message("#> checkpoint['batch'] =", args.checkpoint['batch'], '\n')
                  print("rlogger.filename =", rlogger.filename)
  
                  if len(args.milliseconds) > 1:
                      print('Slow-Ranking Avg Latency =', sum(args.milliseconds[1:]) / len(args.milliseconds[1:]))
  
                  print("\n\n")
  
          print("\n\n")
          print('Avg Latency =', sum(args.milliseconds[1:]) / len(args.milliseconds[1:]))
          print("\n\n")
  
      print('\n\n')
      if qrels:
          assert query_idx + 1 == len(keys) == len(set(keys))
          metrics.output_final_metrics(os.path.join(Run.path, 'ranking.metrics'), query_idx, len(queries))
      print('\n\n')
  