old_contents: # Copyright 2015 Google Inc. 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.
  # ==============================================================================
  """Tests for tensorflow.ops.bicast_op."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  
  import numpy as np
  import tensorflow as tf
  
  
  class BitcastTest(tf.test.TestCase):
  
    def _testBitcast(self, x, datatype, shape):
      with self.test_session():
        tf_ans = tf.bitcast(x, datatype)
        out = tf_ans.eval()
        buff_after = np.getbuffer(out)
        buff_before = np.getbuffer(x)
        self.assertEqual(buff_before, buff_after)
        self.assertEqual(tf_ans.get_shape(), shape)
  
    def testSmaller(self):
      x = np.random.rand(3, 2)
      datatype = tf.int8
      shape = [3, 2, 8]
      self._testBitcast(x, datatype, shape)
  
    def testLarger(self):
      x = np.arange(16, dtype=np.int8).reshape([4, 4])
      datatype = tf.int32
      shape = [4]
      self._testBitcast(x, datatype, shape)
  
    def testSameDtype(self):
      x = np.random.rand(3, 4)
      shape = [3, 4]
      self._testBitcast(x, x.dtype, shape)
  
    def testSameSize(self):
      x = np.random.rand(3, 4)
      shape = [3, 4]
      self._testBitcast(x, tf.double, shape)
  
    def testErrors(self):
      x = np.zeros([1, 1], np.int8)
      datatype = tf.int32
      with self.assertRaisesRegexp(ValueError, "Cannot bitcast due to shape"):
        tf.bitcast(x, datatype, None)
  
    def testEmpty(self):
      x = np.ones([], np.int32)
      datatype = tf.int8
      shape = [4]
      self._testBitcast(x, datatype, shape)
  
  
  if __name__ == "__main__":
    tf.test.main()
  
new_contents: # Copyright 2015 Google Inc. 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.
  # ==============================================================================
  """Tests for tf.bitcast."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  
  import numpy as np
  import tensorflow as tf
  
  
  class BitcastTest(tf.test.TestCase):
  
    def _testBitcast(self, x, datatype, shape):
      with self.test_session():
        tf_ans = tf.bitcast(x, datatype)
        out = tf_ans.eval()
        buff_after = np.getbuffer(out)
        buff_before = np.getbuffer(x)
        self.assertEqual(buff_before, buff_after)
        self.assertEqual(tf_ans.get_shape(), shape)
  
    def testSmaller(self):
      x = np.random.rand(3, 2)
      datatype = tf.int8
      shape = [3, 2, 8]
      self._testBitcast(x, datatype, shape)
  
    def testLarger(self):
      x = np.arange(16, dtype=np.int8).reshape([4, 4])
      datatype = tf.int32
      shape = [4]
      self._testBitcast(x, datatype, shape)
  
    def testSameDtype(self):
      x = np.random.rand(3, 4)
      shape = [3, 4]
      self._testBitcast(x, x.dtype, shape)
  
    def testSameSize(self):
      x = np.random.rand(3, 4)
      shape = [3, 4]
      self._testBitcast(x, tf.double, shape)
  
    def testErrors(self):
      x = np.zeros([1, 1], np.int8)
      datatype = tf.int32
      with self.assertRaisesRegexp(ValueError, "Cannot bitcast due to shape"):
        tf.bitcast(x, datatype, None)
  
    def testEmpty(self):
      x = np.ones([], np.int32)
      datatype = tf.int8
      shape = [4]
      self._testBitcast(x, datatype, shape)
  
  
  if __name__ == "__main__":
    tf.test.main()
  
subject: Fix a typo Change: 114582383