"""Tests for tensorflow.ops.reshape_op."""
import tensorflow.python.platform

import numpy as np
import tensorflow as tf

from tensorflow.python.kernel_tests import gradient_checker as gc


class ReshapeTest(tf.test.TestCase):

  def _testReshape(self, x, y, use_gpu=False):
    with self.test_session(use_gpu=use_gpu):
      np_ans = x.reshape(y)
      tf_ans = tf.reshape(x, y)
      out = tf_ans.eval()
      self.assertEqual(tf_ans.get_shape(), out.shape)
      self.assertShapeEqual(np_ans, tf_ans)

  def _testBothReshape(self, x, y):
    self._testReshape(x, y, False)
    self._testReshape(x, y, True)

  def testFloatBasic(self):
    x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
    self._testBothReshape(x, [2, 3])

  def testDoubleBasic(self):
    x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
    self._testBothReshape(x, [2, 3])

  def testInt32Basic(self):
    x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
    self._testBothReshape(x, [2, 3])

  def testSComplexBasic(self):
    x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
    self._testBothReshape(x, [2, 3])

  def testFloatReshapeThreeDimensions(self):
    x = np.arange(1., 28.).reshape([1, 27]).astype(np.float32)
    self._testBothReshape(x, [3, 3, 3])

  def testFloatUnspecifiedDimOnly(self):
    x = np.arange(1., 7.).reshape([6]).astype(np.float32)
    self._testBothReshape(x, [-1])

  def testFloatUnspecifiedDimBegin(self):
    x = np.arange(1., 7.).reshape([6]).astype(np.float32)
    self._testBothReshape(x, [-1, 2])

  def testFloatUnspecifiedDimEnd(self):
    x = np.arange(1., 7.).reshape([6]).astype(np.float32)
    self._testBothReshape(x, [3, -1])

  # TODO(vrv): Add tests for failure conditions once python test_util
  # reports errors.

  def testFloatReshapeGradThreeDimensions(self):
    x = np.arange(1., 25.).reshape([1, 24]).astype(np.float32)
    s = list(np.shape(x))
    with self.test_session():
      input_tensor = tf.constant(x, shape=[2, 3, 4])
      reshape_out = tf.reshape(input_tensor, [1, 8, 3])
      err = gc.ComputeGradientError(input_tensor, s,
                                    reshape_out, s, x_init_value=x)
    print "Reshape gradient error = " % err
    self.assertLess(err, 1e-3)

  def testFloatEmpty(self):
    x = np.empty((0, 0, 0, 0), dtype=np.float32)
    self._testBothReshape(x, [1, 2, 3, 0])
    self._testBothReshape(x, [1, 0, 0, 4])
    self._testBothReshape(x, [0, 0, 0, 0])
    self._testBothReshape(x, [1, 2, 0])
    self._testBothReshape(x, [0, 0, 0])
    self._testBothReshape(x, [1, -1, 5])

  def testErrors(self):
    x = tf.constant(0.0, shape=[1, 0, 3])
    with self.assertRaisesRegexp(
        ValueError, "cannot infer the missing input size"):
      tf.reshape(x, [0, -1, 5])

    y = tf.constant(0.0, shape=[23, 29, 31])
    with self.assertRaisesRegexp(ValueError, "isn't divisible by 17"):
      tf.reshape(y, [17, -1])

  def testPartialShapes(self):
    x = tf.placeholder(tf.float32)

    # Unknown input shape, partial new shape.
    y = tf.reshape(x, [1, 1, -1, 1])
    self.assertEqual([1, 1, None, 1], y.get_shape().as_list())

    # Unknown input shape, unknown new shape.
    y = tf.reshape(x, tf.placeholder(tf.int32))
    self.assertEqual(None, y.get_shape().ndims)

    # Unknown input shape, known rank for new shape.
    y = tf.reshape(x, tf.placeholder(tf.int32, shape=(3,)))
    self.assertEqual([None, None, None], y.get_shape().as_list())


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