text: # Copyright 2017 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.
  # ==============================================================================
  """Benchmarks for `tf.data.experimental.rejection_resample()`."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import time
  
  import numpy as np
  from six.moves import xrange  # pylint: disable=redefined-builtin
  
  from tensorflow.python.client import session
  from tensorflow.python.data.experimental.ops import resampling
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.platform import test
  
  
  def _time_resampling(data_np, target_dist, init_dist, num_to_sample):  # pylint: disable=missing-docstring
    dataset = dataset_ops.Dataset.from_tensor_slices(data_np).repeat()
  
    # Reshape distribution via rejection sampling.
    dataset = dataset.apply(
        resampling.rejection_resample(
            class_func=lambda x: x,
            target_dist=target_dist,
            initial_dist=init_dist,
            seed=142))
  
    options = dataset_ops.Options()
    options.experimental_optimization.apply_default_optimizations = False
    dataset = dataset.with_options(options)
    get_next = dataset_ops.make_one_shot_iterator(dataset).get_next()
  
    with session.Session() as sess:
      start_time = time.time()
      for _ in xrange(num_to_sample):
        sess.run(get_next)
      end_time = time.time()
  
    return end_time - start_time
  
  
  class RejectionResampleBenchmark(test.Benchmark):
    """Benchmarks for `tf.data.experimental.rejection_resample()`."""
  
    def benchmark_resample_performance(self):
      init_dist = [0.25, 0.25, 0.25, 0.25]
      target_dist = [0.0, 0.0, 0.0, 1.0]
      num_classes = len(init_dist)
      # We don't need many samples to test a dirac-delta target distribution
      num_samples = 1000
      data_np = np.random.choice(num_classes, num_samples, p=init_dist)
  
      resample_time = _time_resampling(
          data_np, target_dist, init_dist, num_to_sample=1000)
  
      self.report_benchmark(iters=1000, wall_time=resample_time, name="resample")
  
  
  if __name__ == "__main__":
    test.main()
  