# Copyright 2019 Google LLC. 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
#
#     https://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.

"""Runs a batch job for performing Tensorflow Model Analysis."""

# pytype: skip-file

from __future__ import absolute_import, division, print_function

import argparse

import tensorflow as tf
import tensorflow_model_analysis as tfma
from tensorflow_model_analysis.evaluators import evaluator

import apache_beam as beam
from apache_beam.io.gcp.bigquery import ReadFromBigQuery
from apache_beam.metrics.metric import MetricsFilter
from apache_beam.testing.load_tests.load_test_metrics_utils import MeasureTime
from apache_beam.testing.load_tests.load_test_metrics_utils import MetricsReader
from trainer import taxi


def process_tfma(
    schema_file,
    big_query_table=None,
    eval_model_dir=None,
    max_eval_rows=None,
    pipeline_args=None,
    publish_to_bq=False,
    project=None,
    metrics_table=None,
    metrics_dataset=None):
  """Runs a batch job to evaluate the eval_model against the given input.

  Args:
  schema_file: A file containing a text-serialized Schema that describes the
      eval data.
  big_query_table: A BigQuery table name specified as DATASET.TABLE which
      should be the input for evaluation. This can only be set if input_csv is
      None.
  eval_model_dir: A directory where the eval model is located.
  max_eval_rows: Number of rows to query from BigQuery.
  pipeline_args: additional DataflowRunner or DirectRunner args passed to
  the beam pipeline.
  publish_to_bq:
  project:
  metrics_dataset:
  metrics_table:

  Raises:
  ValueError: if input_csv and big_query_table are not specified correctly.
  """

  if big_query_table is None:
    raise ValueError('--big_query_table should be provided.')

  slice_spec = [
      tfma.slicer.SingleSliceSpec(),
      tfma.slicer.SingleSliceSpec(columns=['trip_start_hour'])
  ]
  metrics_namespace = metrics_table

  schema = taxi.read_schema(schema_file)

  eval_shared_model = tfma.default_eval_shared_model(
      eval_saved_model_path=eval_model_dir,
      add_metrics_callbacks=[
          tfma.post_export_metrics.calibration_plot_and_prediction_histogram(),
          tfma.post_export_metrics.auc_plots()
      ])

  metrics_monitor = None
  if publish_to_bq:
    metrics_monitor = MetricsReader(
        publish_to_bq=publish_to_bq,
        project_name=project,
        bq_table=metrics_table,
        bq_dataset=metrics_dataset,
        namespace=metrics_namespace,
        filters=MetricsFilter().with_namespace(metrics_namespace))

  pipeline = beam.Pipeline(argv=pipeline_args)

  query = taxi.make_sql(big_query_table, max_eval_rows, for_eval=True)
  raw_feature_spec = taxi.get_raw_feature_spec(schema)
  raw_data = (
      pipeline
      | 'ReadBigQuery' >> ReadFromBigQuery(
          query=query, project=project, use_standard_sql=True)
      | 'Measure time: Start' >> beam.ParDo(MeasureTime(metrics_namespace))
      | 'CleanData' >>
      beam.Map(lambda x: (taxi.clean_raw_data_dict(x, raw_feature_spec))))

  # Examples must be in clean tf-example format.
  coder = taxi.make_proto_coder(schema)
  # Prepare arguments for Extract, Evaluate and Write steps
  extractors = tfma.default_extractors(
      eval_shared_model=eval_shared_model,
      slice_spec=slice_spec,
      desired_batch_size=None,
      materialize=False)

  evaluators = tfma.default_evaluators(
      eval_shared_model=eval_shared_model,
      desired_batch_size=None,
      num_bootstrap_samples=1)
  _ = (
      raw_data
      | 'ToSerializedTFExample' >> beam.Map(coder.encode)
      | 'Extract Results' >> tfma.InputsToExtracts()
      | 'Extract and evaluate' >> tfma.ExtractAndEvaluate(
          extractors=extractors, evaluators=evaluators)
      | 'Map Evaluations to PCollection' >> MapEvalToPCollection()
      | 'Measure time: End' >> beam.ParDo(MeasureTime(metrics_namespace)))
  result = pipeline.run()
  result.wait_until_finish()
  if metrics_monitor:
    metrics_monitor.publish_metrics(result)


@beam.ptransform_fn
@beam.typehints.with_input_types(evaluator.Evaluation)
@beam.typehints.with_output_types(beam.typehints.Any)
def MapEvalToPCollection(  # pylint: disable=invalid-name
    evaluation):
  return evaluation['metrics']


def main():
  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)

  parser = argparse.ArgumentParser()

  parser.add_argument(
      '--eval_model_dir',
      help='Input path to the model which will be evaluated.')
  parser.add_argument(
      '--big_query_table',
      help='BigQuery path to input examples which will be evaluated.')
  parser.add_argument(
      '--max_eval_rows',
      help='Maximum number of rows to evaluate on.',
      default=None,
      type=int)
  parser.add_argument(
      '--schema_file', help='File holding the schema for the input data')
  parser.add_argument(
      '--publish_to_big_query',
      help='Whether to publish to BQ',
      default=None,
      type=bool)
  parser.add_argument(
      '--metrics_dataset', help='BQ dataset', default=None, type=str)
  parser.add_argument(
      '--metrics_table',
      help='BQ table for storing metrics',
      default=None,
      type=str)
  parser.add_argument(
      '--metric_reporting_project',
      help='BQ table project',
      default=None,
      type=str)

  known_args, pipeline_args = parser.parse_known_args()

  process_tfma(
      big_query_table=known_args.big_query_table,
      eval_model_dir=known_args.eval_model_dir,
      max_eval_rows=known_args.max_eval_rows,
      schema_file=known_args.schema_file,
      pipeline_args=pipeline_args,
      publish_to_bq=known_args.publish_to_big_query,
      metrics_table=known_args.metrics_table,
      metrics_dataset=known_args.metrics_dataset,
      project=known_args.metric_reporting_project)


if __name__ == '__main__':
  main()
