# 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 conv."""
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_greater_tests(options):
  """Make a set of tests to do greater."""

  test_parameters = [{
      "input_dtype": [tf.float32, tf.int32, tf.int64],
      "input_shape_pair": [([1, 1, 1, 3], [1, 1, 1, 3]),
                           ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]),
                           ([5, 5], [1]), ([10], [2, 4, 10])],
  }]

  def build_graph(parameters):
    """Build the greater op testing graph."""
    input_value1 = tf.compat.v1.placeholder(
        dtype=parameters["input_dtype"],
        name="input1",
        shape=parameters["input_shape_pair"][0])
    input_value2 = tf.compat.v1.placeholder(
        dtype=parameters["input_dtype"],
        name="input2",
        shape=parameters["input_shape_pair"][1])
    out = tf.greater(input_value1, input_value2)
    return [input_value1, input_value2], [out]

  def build_inputs(parameters, sess, inputs, outputs):
    input_value1 = create_tensor_data(parameters["input_dtype"],
                                      parameters["input_shape_pair"][0])
    input_value2 = create_tensor_data(parameters["input_dtype"],
                                      parameters["input_shape_pair"][1])
    return [input_value1, input_value2], sess.run(
        outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))

  make_zip_of_tests(
      options,
      test_parameters,
      build_graph,
      build_inputs,
      expected_tf_failures=3)
