# Copyright 2016 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
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# ==============================================================================

"""Tests for the SWIG-wrapped quantize training rewriting."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf


class PywrapQuantizeTrainingTest(tf.test.TestCase):

  # Mainly to verify the python interface is working.
  # More tests for this function can be found in the related c++ tests.
  def testQuantizeTraining(self):
    with tf.Session() as sess:
      a = tf.constant(6.0, shape=[1, 1])
      b = tf.constant(7.0, shape=[1, 1])
      c = tf.matmul(a, b, name='matmul')

      self.assertEquals(len(sess.graph_def.node), 3)

      result = tf.train.do_quantize_training_on_graphdef(
          sess.graph_def, 8)

      # 2 convert ops are added so it should have 5 nodes now.
      self.assertEquals(len(result.node), 5)

      self.assertEquals(c.eval(), 42)

if __name__ == '__main__':
  tf.test.main()
