text: # 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 where."""
  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_where_tests(options):
    """Make a set of tests to do where."""
  
    test_parameters = [
        {
            "input_dtype": [tf.float32, tf.int32],
            "input_shape_set": [([1, 2, 3, 4], [1, 2, 3, 4]),],
            "use_where_v2": [False, True],
            "fully_quantize": [False],
        },
        {
            "input_dtype": [tf.float32, tf.int32],
            "input_shape_set": [([], []),],
            "use_where_v2": [],
            "fully_quantize": [False],
        },
        {
            "input_dtype": [tf.float32],
            "input_shape_set": [
                ([1, 2, 3, 4], [1, 2, 3, 4]),
                ([], []),
            ],
            "use_where_v2": [False, True],
            "fully_quantize": [True],
        },
    ]
  
    # High dimension broadcasting support in MLIR converter.
    if options.use_experimental_converter:
      test_parameters = test_parameters + [
          {
              "input_dtype": [tf.float32, tf.int32],
              "input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1]),
                                  ([8, 7, 6, 5, 4, 3, 2, 1], [None, 3, 2, 1]),
                                  ([8, 7, 6, 5, None, 3, 2, 1], [None, 3, 2, 1])],
              "use_where_v2": [True],
              "fully_quantize": [False],
              "dynamic_size_value": [4, 1],
          },
          {
              "input_dtype": [tf.float32],
              "input_shape_set": [([8, 7, 6, 5, 4, 3, 2, 1], [4, 3, 2, 1])],
              "use_where_v2": [True],
              "fully_quantize": [True],
              "dynamic_size_value": [4],
          },
          {
              "input_dtype": [tf.float32, tf.int32],
              "input_shape_set": [([], []), ([1], []), ([], [1])],
              "use_where_v2": [False, True],
              "fully_quantize": [False],
          },
      ]
  
    def populate_dynamic_shape(parameters, input_shape):
      return [
          parameters["dynamic_size_value"] if x is None else x
          for x in input_shape
      ]
  
    def build_graph(parameters):
      """Build the where op testing graph."""
      input_value1 = tf.compat.v1.placeholder(
          dtype=parameters["input_dtype"],
          name="input2",
          shape=parameters["input_shape_set"][0])
      input_value2 = tf.compat.v1.placeholder(
          dtype=parameters["input_dtype"],
          name="input3",
          shape=parameters["input_shape_set"][1])
      less = tf.less(input_value1, input_value2)
      where = tf.where_v2 if parameters["use_where_v2"] else tf.where
      out = where(less, input_value1, input_value2)
      return [input_value1, input_value2], [out]
  
    def build_inputs(parameters, sess, inputs, outputs):
      input_shape_1 = populate_dynamic_shape(parameters,
                                             parameters["input_shape_set"][0])
      input_shape_2 = populate_dynamic_shape(parameters,
                                             parameters["input_shape_set"][1])
  
      input_value1 = create_tensor_data(
          parameters["input_dtype"], input_shape_1, min_value=-1, max_value=1)
      input_value2 = create_tensor_data(
          parameters["input_dtype"], input_shape_2, min_value=-1, max_value=1)
      return [input_value1, input_value2], sess.run(
          outputs, feed_dict=dict(zip(inputs, [input_value1, input_value2])))
  
    if options.use_experimental_converter:
      expected_tf_failures = 4
    else:
      expected_tf_failures = 0
  
    make_zip_of_tests(
        options,
        test_parameters,
        build_graph,
        build_inputs,
        expected_tf_failures=expected_tf_failures)
  