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"""Benchmark test for the batch-only MLTransform Generate Vocab pipeline."""

import logging

from apache_beam.examples.ml_transform import mltransform_generate_vocab
from apache_beam.testing.load_tests.dataflow_cost_benchmark import DataflowCostBenchmark


class MLTransformGenerateVocabBenchmarkTest(DataflowCostBenchmark):
  """Runs the MLTransform vocab generation pipeline as a cost benchmark."""
  def __init__(self):
    # Namespace used by benchmark dashboards.
    self.metrics_namespace = 'BeamML_MLTransformVocab'
    # Monitor throughput on text rows after empty strings are filtered.
    super().__init__(
        metrics_namespace=self.metrics_namespace,
        is_streaming=False,
        pcollection='DropEmptyText.out0')

  def test(self):
    """Execute the MLTransform vocab generation pipeline for benchmarking."""
    extra_opts = {
        'input_file': self.pipeline.get_option('input_file'),
        'output_vocab': self.pipeline.get_option('output_vocab'),
        'artifact_location': self.pipeline.get_option('artifact_location'),
        'columns': self.pipeline.get_option('columns'),
        'vocab_size': self.pipeline.get_option('vocab_size'),
        'min_frequency': self.pipeline.get_option('min_frequency'),
        'lowercase': self.pipeline.get_option('lowercase'),
        'input_expand_factor': self.pipeline.get_option('input_expand_factor'),
    }

    self.result = mltransform_generate_vocab.run(
        self.pipeline.get_full_options_as_args(**extra_opts))


if __name__ == '__main__':
  logging.basicConfig(level=logging.INFO)
  MLTransformGenerateVocabBenchmarkTest().run()
