# 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 embedding_lookup."""
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_embedding_lookup_tests(options):
  """Make a set of tests to do gather."""

  test_parameters = [
      {
          "params_dtype": [tf.float32],
          "params_shape": [[10], [10, 10]],
          "ids_dtype": [tf.int32],
          "ids_shape": [[3], [5]],
      },
  ]

  def build_graph(parameters):
    """Build the gather op testing graph."""
    params = tf.compat.v1.placeholder(
        dtype=parameters["params_dtype"],
        name="params",
        shape=parameters["params_shape"])
    ids = tf.compat.v1.placeholder(
        dtype=parameters["ids_dtype"],
        name="ids",
        shape=parameters["ids_shape"])
    out = tf.nn.embedding_lookup(params, ids)
    return [params, ids], [out]

  def build_inputs(parameters, sess, inputs, outputs):
    params = create_tensor_data(parameters["params_dtype"],
                                parameters["params_shape"])
    ids = create_tensor_data(parameters["ids_dtype"], parameters["ids_shape"],
                             0, parameters["params_shape"][0] - 1)
    return [params, ids], sess.run(
        outputs, feed_dict=dict(zip(inputs, [params, ids])))

  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)