# 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 zeros_like."""
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()
def make_zeros_like_tests(options):
  """Make a set of tests to do zeros_like."""

  test_parameters = [{
      "input_dtype": [tf.float32, tf.int32, tf.int64],
      "input_shape": [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]],
  }]

  def build_graph(parameters):
    """Build the zeros_like op testing graph."""
    input_tensor = tf.compat.v1.placeholder(
        dtype=parameters["input_dtype"],
        name="input",
        shape=parameters["input_shape"])
    zeros = tf.zeros_like(input_tensor)
    # This maximum node is so that converter can perform the
    # constants-propagation through the above zeros_like, which it can't do if
    # the output of the zeros_like as an output of the whole graphs (graph
    # outputs can't be constants). If converter does not perform such
    # constants-propagation then the resulting tflite graph retains the
    # zeros_like as a Fill op, which is unsupported by TFLite, even as a custom
    # op.
    out = tf.maximum(zeros, input_tensor)
    return [input_tensor], [out]

  def build_inputs(parameters, sess, inputs, outputs):
    values = create_tensor_data(parameters["input_dtype"],
                                parameters["input_shape"])
    return [values], sess.run(outputs, feed_dict=dict(zip(inputs, [values])))

  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)

541c83c6b1b27d8685c47f4487dcf9aa9b87ad8c
<mask token>

<mask token>


@register_make_test_function()
def make_zeros_like_tests(options):
    """Make a set of tests to do zeros_like."""
    test_parameters = [{'input_dtype': [tf.float32, tf.int32, tf.int64],
        'input_shape': [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]]}]

    def build_graph(parameters):
        """Build the zeros_like op testing graph."""
        input_tensor = tf.compat.v1.placeholder(dtype=parameters[
            'input_dtype'], name='input', shape=parameters['input_shape'])
        zeros = tf.zeros_like(input_tensor)
        out = tf.maximum(zeros, input_tensor)
        return [input_tensor], [out]

    def build_inputs(parameters, sess, inputs, outputs):
        values = create_tensor_data(parameters['input_dtype'], parameters[
            'input_shape'])
        return [values], sess.run(outputs, feed_dict=dict(zip(inputs, [
            values])))
    make_zip_of_tests(options, test_parameters, build_graph, build_inputs)

<mask token>
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()
def make_zeros_like_tests(options):
    """Make a set of tests to do zeros_like."""
    test_parameters = [{'input_dtype': [tf.float32, tf.int32, tf.int64],
        'input_shape': [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]]}]

    def build_graph(parameters):
        """Build the zeros_like op testing graph."""
        input_tensor = tf.compat.v1.placeholder(dtype=parameters[
            'input_dtype'], name='input', shape=parameters['input_shape'])
        zeros = tf.zeros_like(input_tensor)
        out = tf.maximum(zeros, input_tensor)
        return [input_tensor], [out]

    def build_inputs(parameters, sess, inputs, outputs):
        values = create_tensor_data(parameters['input_dtype'], parameters[
            'input_shape'])
        return [values], sess.run(outputs, feed_dict=dict(zip(inputs, [
            values])))
    make_zip_of_tests(options, test_parameters, build_graph, build_inputs)

# 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 zeros_like."""
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()
def make_zeros_like_tests(options):
  """Make a set of tests to do zeros_like."""

  test_parameters = [{
      "input_dtype": [tf.float32, tf.int32, tf.int64],
      "input_shape": [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]],
  }]

  def build_graph(parameters):
    """Build the zeros_like op testing graph."""
    input_tensor = tf.compat.v1.placeholder(
        dtype=parameters["input_dtype"],
        name="input",
        shape=parameters["input_shape"])
    zeros = tf.zeros_like(input_tensor)
    # This maximum node is so that converter can perform the
    # constants-propagation through the above zeros_like, which it can't do if
    # the output of the zeros_like as an output of the whole graphs (graph
    # outputs can't be constants). If converter does not perform such
    # constants-propagation then the resulting tflite graph retains the
    # zeros_like as a Fill op, which is unsupported by TFLite, even as a custom
    # op.
    out = tf.maximum(zeros, input_tensor)
    return [input_tensor], [out]

  def build_inputs(parameters, sess, inputs, outputs):
    values = create_tensor_data(parameters["input_dtype"],
                                parameters["input_shape"])
    return [values], sess.run(outputs, feed_dict=dict(zip(inputs, [values])))

  make_zip_of_tests(options, test_parameters, build_graph, build_inputs)
