text: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Benchmark for the experimental `MatchingFilesDataset`."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import shutil
  import tempfile
  import time
  
  import numpy as np
  
  from tensorflow.python.client import session
  from tensorflow.python.data.experimental.ops import matching_files
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.platform import test
  
  
  class MatchingFilesBenchmark(test.Benchmark):
    """Benchmark for the experimental `MatchingFilesDataset`."""
  
    def benchmarkNestedDirectories(self):
      tmp_dir = tempfile.mkdtemp()
      width = 500
      depth = 10
      for i in range(width):
        for j in range(depth):
          new_base = os.path.join(tmp_dir, str(i),
                                  *[str(dir_name) for dir_name in range(j)])
          os.makedirs(new_base)
          child_files = ['a.py', 'b.pyc'] if j < depth - 1 else ['c.txt', 'd.log']
          for f in child_files:
            filename = os.path.join(new_base, f)
            open(filename, 'w').close()
  
      patterns = [
          os.path.join(tmp_dir, os.path.join(*['**'
                                               for _ in range(depth)]), suffix)
          for suffix in ['*.txt', '*.log']
      ]
  
      deltas = []
      iters = 3
      for _ in range(iters):
        with ops.Graph().as_default():
          dataset = matching_files.MatchingFilesDataset(patterns)
          next_element = dataset.make_one_shot_iterator().get_next()
  
          with session.Session() as sess:
            sub_deltas = []
            while True:
              try:
                start = time.time()
                sess.run(next_element)
                end = time.time()
                sub_deltas.append(end - start)
              except errors.OutOfRangeError:
                break
            deltas.append(sub_deltas)
  
      median_deltas = np.median(deltas, axis=0)
      print('Nested directory size (width*depth): %d*%d Median wall time: '
            '%fs (read first filename), %fs (read second filename), avg %fs'
            ' (read %d more filenames)' %
            (width, depth, median_deltas[0], median_deltas[1],
             np.average(median_deltas[2:]), len(median_deltas) - 2))
      self.report_benchmark(
          iters=iters,
          wall_time=np.sum(median_deltas),
          extras={
              'read first file:':
                  median_deltas[0],
              'read second file:':
                  median_deltas[1],
              'avg time for reading %d more filenames:' %
              (len(median_deltas) - 2):
                  np.average(median_deltas[2:])
          },
          name='dataset_nested_directory(%d*%d)' %
          (width, depth))
  
      shutil.rmtree(tmp_dir, ignore_errors=True)
  
  
  if __name__ == '__main__':
    test.main()
  