text: # Copyright 2017 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.
  # ==============================================================================
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import tempfile
  
  import tensorflow as tf
  from tensorflow.lite.toco import model_flags_pb2
  from tensorflow.lite.toco import toco_flags_pb2
  from tensorflow.lite.toco import types_pb2
  from tensorflow.python.platform import googletest
  from tensorflow.python.platform import resource_loader
  
  
  def TensorName(x):
    """Get the canonical (non foo:0 name)."""
    return x.name.split(":")[0]
  
  
  class TocoFromProtosTest(googletest.TestCase):
  
    def _run(self, sess, in_tensor, out_tensor, should_succeed):
      """Use toco binary to check conversion from graphdef to tflite.
  
      Args:
        sess: Active TensorFlow session containing graph.
        in_tensor: TensorFlow tensor to use as input.
        out_tensor: TensorFlow tensor to use as output.
        should_succeed: Whether this is a valid conversion.
      """
      # Build all protos and extract graphdef
      graph_def = sess.graph_def
      toco_flags = toco_flags_pb2.TocoFlags()
      toco_flags.input_format = toco_flags_pb2.TENSORFLOW_GRAPHDEF
      toco_flags.output_format = toco_flags_pb2.TFLITE
      toco_flags.inference_input_type = types_pb2.FLOAT
      toco_flags.inference_type = types_pb2.FLOAT
      toco_flags.allow_custom_ops = True
      model_flags = model_flags_pb2.ModelFlags()
      input_array = model_flags.input_arrays.add()
      input_array.name = TensorName(in_tensor)
      input_array.shape.dims.extend(map(int, in_tensor.shape))
      model_flags.output_arrays.append(TensorName(out_tensor))
      # Shell out to run toco (in case it crashes)
      with tempfile.NamedTemporaryFile() as fp_toco, \
             tempfile.NamedTemporaryFile() as fp_model, \
             tempfile.NamedTemporaryFile() as fp_input, \
             tempfile.NamedTemporaryFile() as fp_output:
        fp_model.write(model_flags.SerializeToString())
        fp_toco.write(toco_flags.SerializeToString())
        fp_input.write(graph_def.SerializeToString())
        fp_model.flush()
        fp_toco.flush()
        fp_input.flush()
        tflite_bin = resource_loader.get_path_to_datafile("toco_from_protos.par")
        cmdline = " ".join([
            tflite_bin, fp_model.name, fp_toco.name, fp_input.name, fp_output.name
        ])
        exitcode = os.system(cmdline)
        if exitcode == 0:
          stuff = fp_output.read()
          self.assertEqual(stuff is not None, should_succeed)
        else:
          self.assertFalse(should_succeed)
  
    def test_toco(self):
      """Run a couple of TensorFlow graphs against TOCO through the python bin."""
      with tf.compat.v1.Session() as sess:
        img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3))
        val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.])
        out = tf.identity(val, name="out")
        out2 = tf.sin(val, name="out2")
        # This is a valid mdoel
        self._run(sess, img, out, True)
        # This uses an invalid function.
        # TODO(aselle): Check to make sure a warning is included.
        self._run(sess, img, out2, True)
        # This is an identity graph, which doesn't work
        self._run(sess, img, img, False)
  
  
  if __name__ == "__main__":
    googletest.main()
  