# Copyright 2019 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.
# ==============================================================================
"""Test configs for unfused_gru."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow.compat.v1 as tf
from tensorflow.lite.testing.zip_test_utils import create_tensor_data
from tensorflow.lite.testing.zip_test_utils import make_zip_of_tests
from tensorflow.lite.testing.zip_test_utils import register_make_test_function


@register_make_test_function()
def make_unfused_gru_tests(options):
  """Make a set of tests for unfused gru op."""

  test_parameters = [{
      "units": [2, 5],
      "batch_size": [1, 2],
      "time": [3],
  }]

  def build_graph(parameters):
    """Build the graph for unfused_gru."""
    inputs = [
        tf.compat.v1.placeholder(
            tf.float32, [parameters["batch_size"], parameters["units"]])
        for _ in range(parameters["time"])
    ]
    cell_fw = tf.compat.v1.nn.rnn_cell.GRUCell(parameters["units"])
    cell_bw = tf.compat.v1.nn.rnn_cell.GRUCell(parameters["units"])
    outputs, _, _ = tf.compat.v1.nn.static_bidirectional_rnn(
        cell_fw, cell_bw, inputs, dtype=tf.float32)

    return inputs, outputs

  def build_inputs(parameters, sess, inputs, outputs):
    """Build the inputs for unfused_gru."""
    input_values = [
        create_tensor_data(tf.float32,
                           [parameters["batch_size"], parameters["units"]])
        for _ in range(parameters["time"])
    ]
    init = tf.compat.v1.global_variables_initializer()
    sess.run(init)
    return input_values, sess.run(
        outputs, feed_dict=dict(zip(inputs, input_values)))

  make_zip_of_tests(
      options,
      test_parameters,
      build_graph,
      build_inputs,
      use_frozen_graph=True)
