# Copyright 2020 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 KPL implementation of vocabulary columns + indicator from lists with varying-length inputs."""

import tensorflow as tf

import keras
from tensorflow.python.eager.def_function import function as tf_function
from keras.layers.preprocessing import category_encoding
from keras.layers.preprocessing import string_lookup
from keras.layers.preprocessing.benchmarks import feature_column_benchmark as fc_bm

# This is required as of 3/2021 because otherwise we drop into graph mode.
tf.compat.v1.enable_v2_behavior()

NUM_REPEATS = 10
BATCH_SIZES = [32, 256]


def embedding_varlen(batch_size, max_length):
  """Benchmark a variable-length embedding."""
  # Data and constants.
  vocab_size = 32768
  vocab = fc_bm.create_vocabulary(vocab_size)
  data = fc_bm.create_string_data(
      max_length, batch_size * NUM_REPEATS, vocab, pct_oov=0.15)

  # Keras implementation
  model = keras.Sequential()
  model.add(
      keras.Input(
          shape=(max_length,), name="data", ragged=True, dtype=tf.string))
  model.add(string_lookup.StringLookup(vocabulary=vocab, mask_token=None))
  model.add(
      category_encoding.CategoryEncoding(
          num_tokens=vocab_size + 1, output_mode="count"))

  # FC implementation
  fc = tf.feature_column.indicator_column(
      tf.feature_column.sequence_categorical_column_with_vocabulary_list(
          key="data", vocabulary_list=vocab, num_oov_buckets=1))

  # Wrap the FC implementation in a tf.function for a fair comparison
  @tf_function()
  def fc_fn(tensors):
    fc.transform_feature(tf.__internal__.feature_column.FeatureTransformationCache(tensors), None)

  # Benchmark runs
  keras_data = {"data": data}
  k_avg_time = fc_bm.run_keras(keras_data, model, batch_size, NUM_REPEATS)

  fc_data = {"data": data.to_sparse()}
  fc_avg_time = fc_bm.run_fc(fc_data, fc_fn, batch_size, NUM_REPEATS)

  return k_avg_time, fc_avg_time


class BenchmarkLayer(fc_bm.LayerBenchmark):
  """Benchmark the layer forward pass."""

  def benchmark_layer(self):
    for batch in BATCH_SIZES:
      name = "vocab_list_indicator|varlen|batch_%s" % batch
      k_time, f_time = embedding_varlen(batch_size=batch, max_length=256)
      self.report(name, k_time, f_time, NUM_REPEATS)


if __name__ == "__main__":
  tf.test.main()
