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.Dataset.unbatch()`."""
  from tensorflow.python.data.benchmarks import benchmark_base
  from tensorflow.python.data.ops import dataset_ops
  
  
  class UnbatchBenchmark(benchmark_base.DatasetBenchmarkBase):
    """Benchmarks for `tf.data.Dataset.unbatch()`."""
  
    def benchmark_native_unbatch(self):
      batch_sizes = [1, 2, 5, 10, 20, 50]
      num_elements = 10000
  
      for batch_size in batch_sizes:
        dataset = dataset_ops.Dataset.from_tensors("element").repeat(None)
        dataset = dataset.batch(batch_size)
        dataset = dataset.unbatch()
  
        self.run_and_report_benchmark(
            dataset=dataset,
            num_elements=num_elements,
            iters=5,
            extras={
                "model_name": "unbatch.benchmark.1",
                "parameters": "%d" % batch_size,
            },
            name="native_batch_size_%d" % batch_size)
  
    # Include a benchmark of the previous `unbatch()` implementation that uses
    # a composition of more primitive ops. Eventually we'd hope to generate code
    # that is as good in both cases.
    def benchmark_old_unbatch_implementation(self):
      batch_sizes = [1, 2, 5, 10, 20, 50]
      num_elements = 10000
  
      for batch_size in batch_sizes:
        dataset = dataset_ops.Dataset.from_tensors("element").repeat(None)
        dataset = dataset.batch(batch_size)
        dataset = dataset.flat_map(dataset_ops.Dataset.from_tensor_slices)
  
        self.run_and_report_benchmark(
            dataset=dataset,
            num_elements=num_elements,
            iters=5,
            extras={
                "model_name": "unbatch.benchmark.2",
                "parameters": "%d" % batch_size,
            },
            name="unfused_batch_size_%d" % batch_size)
  
  
  if __name__ == "__main__":
    benchmark_base.test.main()
  