text: # 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.
  # ==============================================================================
  """Test configs for batchmatmul."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow 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("make_batchmatmul_tests")
  def make_batchmatmul_tests(options):
    """Make a set of tests to do basic batch matrix multiply."""
  
    test_parameters = [
        {
            "dtype": [tf.float32],
            "shapes": [((3, 4, 7), (7, 9), (3, 4, 7), (7, 9)),
                       ((None, 4, 5), (None, 5, 6), (3, 4, 5), (3, 5, 6)),
                       ((None, 1, 3, 4), (None, 4, 2), (2, 1, 3, 4), (5, 4, 2))],
            "adjoint_b": [False, True],
            "adjoint_a": [False, True],
            "rhs_constant": [False],
            "fully_quantize": [False, True],
        },
    ]
  
    def swap_last_two_dims(*args):
      """Return a tuple with the last two dimensions swapped."""
      return args[:-2] + (args[-1],) + (args[-2],)
  
    def build_graph(parameters):
      """Build a simple graph with BatchMatMul."""
      placeholder0_shape = parameters["shapes"][0]
      adj_a = parameters["adjoint_a"]
      adj_b = parameters["adjoint_b"]
      rhs_constant = parameters["rhs_constant"]
      if adj_a:
        placeholder0_shape = swap_last_two_dims(*placeholder0_shape)
      input0_tensor = tf.compat.v1.placeholder(
          dtype=parameters["dtype"], shape=placeholder0_shape)
      if rhs_constant:
        if adj_b:
          constant1_shape = swap_last_two_dims(*parameters["shapes"][3])
        else:
          constant1_shape = parameters["shapes"][3]
        data = create_tensor_data(
            parameters["dtype"], constant1_shape, min_value=-1.0, max_value=1.0)
        input1_constant = tf.constant(
            data, shape=constant1_shape, dtype=parameters["dtype"])
        out = tf.matmul(
            input0_tensor, input1_constant, adjoint_a=adj_a, adjoint_b=adj_b)
        return [input0_tensor], [out]
      else:
        if adj_b:
          placeholder1_shape = swap_last_two_dims(*parameters["shapes"][1])
        else:
          placeholder1_shape = parameters["shapes"][1]
        input1_tensor = tf.compat.v1.placeholder(
            dtype=parameters["dtype"], shape=placeholder1_shape)
        out = tf.matmul(
            input0_tensor, input1_tensor, adjoint_a=adj_a, adjoint_b=adj_b)
        return [input0_tensor, input1_tensor], [out]
  
    def build_inputs(parameters, sess, inputs, outputs):
      """Feed inputs, assign variables, and freeze graph."""
      input0_shape = parameters["shapes"][2]
      adj_a = parameters["adjoint_a"]
      adj_b = parameters["adjoint_b"]
      rhs_constant = parameters["rhs_constant"]
      if adj_a:
        input0_shape = swap_last_two_dims(*input0_shape)
      input0_value = create_tensor_data(
          parameters["dtype"], input0_shape, min_value=-1.0, max_value=1.0)
      if rhs_constant:
        output_values = sess.run(
            outputs, feed_dict=dict(zip(inputs, [input0_value])))
        return [input0_value], output_values
      else:
        input1_shape = parameters["shapes"][
            3] if not adj_b else swap_last_two_dims(*parameters["shapes"][3])
        input1_value = create_tensor_data(
            parameters["dtype"], input1_shape, min_value=-1.0, max_value=1.0)
        output_values = sess.run(
            outputs, feed_dict=dict(zip(inputs, [input0_value, input1_value])))
        return [input0_value, input1_value], output_values
  
    options.use_experimental_converter = True
    make_zip_of_tests(
        options,
        test_parameters,
        build_graph,
        build_inputs,
        expected_tf_failures=0)
  