text: from collections import defaultdict
  from pathlib import Path
  from typing import List
  from tqdm import tqdm
  from nboost.stats import ClassStatistics
  from nboost import DATASET_MAP, PKG_PATH
  from nboost.logger import set_logger
  from nboost.benchmark import api
  from nboost.helpers import (
      csv_generator,
      download_file,
      extract_tar_gz,
      count_lines
  )
  
  
  class Benchmarker:
      """Object used to calculate mean reciprocal rank and latency of search
      ranking. This is necessary to compare the current proxy model performance
      relative to the search-api performance without the proxy.
  
      The benchmarker assumes that the dataset has three structures:
          queries: query id, query
          documents: ddc id, document
          query relations (qrels): query id, doc id
      """
      stats = ClassStatistics()
  
      def __init__(self, dataset: str = 'ms_marco', rows: int = 100,
                   connector: str = 'ESConnector', **kwargs):
  
          self.dataset = dataset
          self.rows = rows
          self.connector = getattr(api, connector)(dataset=dataset, **kwargs)  # type: api.Connector
          self.logger = set_logger(self.__class__.__name__)
          self.queries = dict()
          self.qrels = defaultdict(set)
          self.map = DATASET_MAP[dataset]
          self.cache_path = PKG_PATH.joinpath('.cache/datasets/%s' % dataset)
  
      def setup(self):
          self.logger.info('Setting up benchmarker...')
          self.connector.setup(self.yield_csv('choices'), self.map['size'])
          self.add_qrels()
          self.add_queries()
  
      @stats.time_context
      def query_upstream(self, query: str) -> List[int]:
          return self.connector.get_cids(query)
  
      @stats.time_context
      def query_proxy(self, query: str) -> List[int]:
          return self.connector.get_cids(query, proxy=True)
  
      @stats.vars_context
      def record_mrr(self, **kwargs):
          """Record the proxy or upstream mrr"""
  
      def download_and_extract(self):
          file_name = Path(self.map['url']).name
          tar_gz_path = self.cache_path.joinpath(file_name)
          if not self.cache_path.exists():
              self.cache_path.mkdir(parents=True, exist_ok=True)
              self.logger.info('Dowloading to %s' % tar_gz_path)
              download_file(self.map['url'], tar_gz_path)
              self.logger.info('Extracting %s' % tar_gz_path)
              extract_tar_gz(tar_gz_path)
              tar_gz_path.unlink()
  
      def yield_csv(self, file_type: str):
          file_name, indices, delim = self.map[file_type]
          path = self.cache_path.joinpath(file_name)
          num_lines = count_lines(path)
          with tqdm(total=num_lines, desc=path.name) as pbar:
              for row in csv_generator(path, indices, delim):
                  pbar.update()
                  yield row
  
      def add_qrels(self):
          """Add query relations"""
          for qid, doc_id in self.yield_csv('qrels'):
              self.qrels[qid].add(doc_id)
  
      def add_queries(self):
          """Add query id and text"""
          for qid, query in self.yield_csv('queries'):
              self.queries[qid] = query
  
      def benchmark(self):
          """The benchmark method times the doc_id_producers and
          calculates the mrr."""
          # filter queries without relations
          for qid in list(self.queries):
              if qid not in self.qrels:
                  self.queries.pop(qid)
  
          maximum = len(self.queries)
          rows = maximum if self.rows == -1 else min(maximum, self.rows)
  
          with tqdm(total=rows) as pbar:
              for _ in range(rows):
                  qid, query = self.queries.popitem()
  
                  proxy_cids = self.query_proxy(query)
                  proxy_mrr = self.calculate_mrr(qid, proxy_cids)
                  self.record_mrr(proxy_mrr=proxy_mrr)
  
                  upstream_cids = self.query_upstream(query)
                  upstream_mrr = self.calculate_mrr(qid, upstream_cids)
                  self.record_mrr(upstream_mrr=upstream_mrr)
  
                  self.set_progress_bar(pbar)
  
      def set_progress_bar(self, pbar: tqdm):
          """Update the progress bar with current statistics"""
          pbar.set_description(
              'PROXY: {proxy_mrr:.5f} MRR {proxy_ms:.2f} ms/query '
              'DIRECT: {direct_mrr:.5f} MRR {direct_ms:.2f} ms/query'.format(
                  proxy_mrr=self.stats.record['vars']['proxy_mrr']['avg'],
                  direct_mrr=self.stats.record['vars']['upstream_mrr']['avg'],
                  proxy_ms=self.stats.record['time']['query_proxy']['avg'],
                  direct_ms=self.stats.record['time']['query_upstream']['avg']
              )
          )
          pbar.update()
  
      def calculate_mrr(self, qid: str, cids: List[int]):
          """Calculate mean reciprocal rank as the first correct result index"""
          for i, cid in enumerate(cids, 1):
              if cid in self.qrels[qid]:
                  return 1 / i
          return 0
  