text: # Copyright 2016 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.
  # ==============================================================================
  """tensor_util tests."""
  
  # pylint: disable=unused-import
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import re
  import numpy as np
  import tensorflow as tf
  
  
  class FloatDTypeTest(tf.test.TestCase):
  
    def test_assert_same_float_dtype(self):
      self.assertIs(
          tf.float32, tf.contrib.framework.assert_same_float_dtype(None, None))
      self.assertIs(
          tf.float32, tf.contrib.framework.assert_same_float_dtype([], None))
      self.assertIs(
          tf.float32,
          tf.contrib.framework.assert_same_float_dtype([], tf.float32))
      self.assertIs(
          tf.float32,
          tf.contrib.framework.assert_same_float_dtype(None, tf.float32))
      self.assertIs(
          tf.float32,
          tf.contrib.framework.assert_same_float_dtype([None, None], None))
      self.assertIs(
          tf.float32,
          tf.contrib.framework.assert_same_float_dtype([None, None], tf.float32))
  
      const_float = tf.constant(3.0, dtype=tf.float32)
      self.assertIs(
          tf.float32,
          tf.contrib.framework.assert_same_float_dtype([const_float], tf.float32))
      self.assertRaises(
          ValueError,
          tf.contrib.framework.assert_same_float_dtype, [const_float], tf.int32)
  
      sparse_float = tf.SparseTensor(
          tf.constant([[111], [232]], tf.int64),
          tf.constant([23.4, -43.2], tf.float32),
          tf.constant([500], tf.int64))
      self.assertIs(tf.float32, tf.contrib.framework.assert_same_float_dtype(
          [sparse_float], tf.float32))
      self.assertRaises(
          ValueError,
          tf.contrib.framework.assert_same_float_dtype, [sparse_float], tf.int32)
      self.assertRaises(
          ValueError, tf.contrib.framework.assert_same_float_dtype,
          [const_float, None, sparse_float], tf.float64)
  
      self.assertIs(
          tf.float32,
          tf.contrib.framework.assert_same_float_dtype(
              [const_float, sparse_float]))
      self.assertIs(tf.float32, tf.contrib.framework.assert_same_float_dtype(
          [const_float, sparse_float], tf.float32))
  
      const_int = tf.constant(3, dtype=tf.int32)
      self.assertRaises(ValueError, tf.contrib.framework.assert_same_float_dtype,
                        [sparse_float, const_int])
      self.assertRaises(ValueError, tf.contrib.framework.assert_same_float_dtype,
                        [sparse_float, const_int], tf.int32)
      self.assertRaises(ValueError, tf.contrib.framework.assert_same_float_dtype,
                        [sparse_float, const_int], tf.float32)
      self.assertRaises(
          ValueError, tf.contrib.framework.assert_same_float_dtype, [const_int])
  
  
  class AssertLessTest(tf.test.TestCase):
  
    def test_raises_when_equal(self):
      with self.test_session():
        small = tf.constant([1, 2], name="small")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less(small, small)]):
          out = tf.identity(small)
        with self.assertRaisesOpError("small.*small"):
          out.eval()
  
    def test_raises_when_greater(self):
      with self.test_session():
        small = tf.constant([1, 2], name="small")
        big = tf.constant([3, 4], name="big")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less(big, small)]):
          out = tf.identity(small)
        with self.assertRaisesOpError("big.*small"):
          out.eval()
  
    def test_doesnt_raise_when_less(self):
      with self.test_session():
        small = tf.constant([3, 1], name="small")
        big = tf.constant([4, 2], name="big")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less(small, big)]):
          out = tf.identity(small)
        out.eval()
  
    def test_doesnt_raise_when_less_and_broadcastable_shapes(self):
      with self.test_session():
        small = tf.constant([1], name="small")
        big = tf.constant([3, 2], name="big")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less(small, big)]):
          out = tf.identity(small)
        out.eval()
  
    def test_raises_when_less_but_non_broadcastable_shapes(self):
      with self.test_session():
        small = tf.constant([1, 1, 1], name="small")
        big = tf.constant([3, 2], name="big")
        with self.assertRaisesRegexp(ValueError, "broadcast"):
          with tf.control_dependencies(
              [tf.contrib.framework.assert_less(small, big)]):
            out = tf.identity(small)
          out.eval()
  
    def test_doesnt_raise_when_both_empty(self):
      with self.test_session():
        larry = tf.constant([])
        curly = tf.constant([])
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less(larry, curly)]):
          out = tf.identity(larry)
        out.eval()
  
  
  class AssertLessEqualTest(tf.test.TestCase):
  
    def test_doesnt_raise_when_equal(self):
      with self.test_session():
        small = tf.constant([1, 2], name="small")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less_equal(small, small)]):
          out = tf.identity(small)
        out.eval()
  
    def test_raises_when_greater(self):
      with self.test_session():
        small = tf.constant([1, 2], name="small")
        big = tf.constant([3, 4], name="big")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less_equal(big, small)]):
          out = tf.identity(small)
        with self.assertRaisesOpError("big.*small"):
          out.eval()
  
    def test_doesnt_raise_when_less_equal(self):
      with self.test_session():
        small = tf.constant([1, 2], name="small")
        big = tf.constant([3, 2], name="big")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less_equal(small, big)]):
          out = tf.identity(small)
        out.eval()
  
    def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self):
      with self.test_session():
        small = tf.constant([1], name="small")
        big = tf.constant([3, 1], name="big")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less_equal(small, big)]):
          out = tf.identity(small)
        out.eval()
  
    def test_raises_when_less_equal_but_non_broadcastable_shapes(self):
      with self.test_session():
        small = tf.constant([1, 1, 1], name="small")
        big = tf.constant([3, 1], name="big")
        with self.assertRaisesRegexp(ValueError, "broadcast"):
          with tf.control_dependencies(
              [tf.contrib.framework.assert_less_equal(small, big)]):
            out = tf.identity(small)
          out.eval()
  
    def test_doesnt_raise_when_both_empty(self):
      with self.test_session():
        larry = tf.constant([])
        curly = tf.constant([])
        with tf.control_dependencies(
            [tf.contrib.framework.assert_less_equal(larry, curly)]):
          out = tf.identity(larry)
        out.eval()
  
  
  class AssertNegativeTest(tf.test.TestCase):
  
    def test_doesnt_raise_when_negative(self):
      with self.test_session():
        frank = tf.constant([-1, -2], name="frank")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_negative(frank)]):
          out = tf.identity(frank)
        out.eval()
  
    def test_raises_when_positive(self):
      with self.test_session():
        doug = tf.constant([1, 2], name="doug")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_negative(doug)]):
          out = tf.identity(doug)
        with self.assertRaisesOpError("doug"):
          out.eval()
  
    def test_raises_when_zero(self):
      with self.test_session():
        claire = tf.constant([0], name="claire")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_negative(claire)]):
          out = tf.identity(claire)
        with self.assertRaisesOpError("claire"):
          out.eval()
  
    def test_empty_tensor_doesnt_raise(self):
      # A tensor is negative when it satisfies:
      #   For every element x_i in x, x_i < 0
      # and an empty tensor has no elements, so this is trivially satisfied.
      # This is standard set theory.
      with self.test_session():
        empty = tf.constant([], name="empty")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_negative(empty)]):
          out = tf.identity(empty)
        out.eval()
  
  
  class AssertPostiveTest(tf.test.TestCase):
  
    def test_raises_when_negative(self):
      with self.test_session():
        freddie = tf.constant([-1, -2], name="freddie")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_positive(freddie)]):
          out = tf.identity(freddie)
        with self.assertRaisesOpError("freddie"):
          out.eval()
  
    def test_doesnt_raise_when_positive(self):
      with self.test_session():
        remmy = tf.constant([1, 2], name="remmy")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_positive(remmy)]):
          out = tf.identity(remmy)
        out.eval()
  
    def test_raises_when_zero(self):
      with self.test_session():
        meechum = tf.constant([0], name="meechum")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_positive(meechum)]):
          out = tf.identity(meechum)
        with self.assertRaisesOpError("meechum"):
          out.eval()
  
    def test_empty_tensor_doesnt_raise(self):
      # A tensor is positive when it satisfies:
      #   For every element x_i in x, x_i > 0
      # and an empty tensor has no elements, so this is trivially satisfied.
      # This is standard set theory.
      with self.test_session():
        empty = tf.constant([], name="empty")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_positive(empty)]):
          out = tf.identity(empty)
        out.eval()
  
  
  class AssertNonNegativeTest(tf.test.TestCase):
  
    def test_raises_when_negative(self):
      with self.test_session():
        zoe = tf.constant([-1, -2], name="zoe")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_non_negative(zoe)]):
          out = tf.identity(zoe)
        with self.assertRaisesOpError("zoe"):
          out.eval()
  
    def test_doesnt_raise_when_zero_and_positive(self):
      with self.test_session():
        lucas = tf.constant([0, 2], name="lucas")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_non_negative(lucas)]):
          out = tf.identity(lucas)
        out.eval()
  
    def test_empty_tensor_doesnt_raise(self):
      # A tensor is non-negative when it satisfies:
      #   For every element x_i in x, x_i >= 0
      # and an empty tensor has no elements, so this is trivially satisfied.
      # This is standard set theory.
      with self.test_session():
        empty = tf.constant([], name="empty")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_non_negative(empty)]):
          out = tf.identity(empty)
        out.eval()
  
  
  class AssertNonPositiveTest(tf.test.TestCase):
  
    def test_doesnt_raise_when_zero_and_negative(self):
      with self.test_session():
        tom = tf.constant([0, -2], name="tom")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_non_positive(tom)]):
          out = tf.identity(tom)
        out.eval()
  
    def test_raises_when_positive(self):
      with self.test_session():
        rachel = tf.constant([0, 2], name="rachel")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_non_positive(rachel)]):
          out = tf.identity(rachel)
        with self.assertRaisesOpError("rachel"):
          out.eval()
  
    def test_empty_tensor_doesnt_raise(self):
      # A tensor is non-positive when it satisfies:
      #   For every element x_i in x, x_i <= 0
      # and an empty tensor has no elements, so this is trivially satisfied.
      # This is standard set theory.
      with self.test_session():
        empty = tf.constant([], name="empty")
        with tf.control_dependencies(
            [tf.contrib.framework.assert_non_positive(empty)]):
          out = tf.identity(empty)
        out.eval()
  
  
  class AssertScalarIntTest(tf.test.TestCase):
  
    def test_assert_scalar_int(self):
      tf.contrib.framework.assert_scalar_int(tf.constant(3, dtype=tf.int32))
      tf.contrib.framework.assert_scalar_int(tf.constant(3, dtype=tf.int64))
      with self.assertRaisesRegexp(ValueError, "Unexpected type"):
        tf.contrib.framework.assert_scalar_int(tf.constant(3, dtype=tf.float32))
      with self.assertRaisesRegexp(ValueError, "Unexpected shape"):
        tf.contrib.framework.assert_scalar_int(
            tf.constant([3, 4], dtype=tf.int32))
  
  
  class IsStrictlyIncreasingTest(tf.test.TestCase):
  
    def test_constant_tensor_is_not_strictly_increasing(self):
      with self.test_session():
        self.assertFalse(
            tf.contrib.framework.is_strictly_increasing([1, 1, 1]).eval())
  
    def test_decreasing_tensor_is_not_strictly_increasing(self):
      with self.test_session():
        self.assertFalse(
            tf.contrib.framework.is_strictly_increasing([1, 0, -1]).eval())
  
    def test_2d_decreasing_tensor_is_not_strictly_increasing(self):
      with self.test_session():
        self.assertFalse(
            tf.contrib.framework.is_strictly_increasing([[1, 3], [2, 4]]).eval())
  
    def test_increasing_tensor_is_increasing(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_strictly_increasing([1, 2, 3]).eval())
  
    def test_increasing_rank_two_tensor(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_strictly_increasing([[-1, 2], [3, 4]]).eval())
  
    def test_tensor_with_one_element_is_strictly_increasing(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_strictly_increasing([1]).eval())
  
    def test_empty_tensor_is_strictly_increasing(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_strictly_increasing([]).eval())
  
  
  class IsNonDecreasingTest(tf.test.TestCase):
  
    def test_constant_tensor_is_non_decreasing(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_non_decreasing([1, 1, 1]).eval())
  
    def test_decreasing_tensor_is_not_non_decreasing(self):
      with self.test_session():
        self.assertFalse(
            tf.contrib.framework.is_non_decreasing([3, 2, 1]).eval())
  
    def test_2d_decreasing_tensor_is_not_non_decreasing(self):
      with self.test_session():
        self.assertFalse(
            tf.contrib.framework.is_non_decreasing([[1, 3], [2, 4]]).eval())
  
    def test_increasing_rank_one_tensor_is_non_decreasing(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_non_decreasing([1, 2, 3]).eval())
  
    def test_increasing_rank_two_tensor(self):
      with self.test_session():
        self.assertTrue(
            tf.contrib.framework.is_non_decreasing([[-1, 2], [3, 3]]).eval())
  
    def test_tensor_with_one_element_is_non_decreasing(self):
      with self.test_session():
        self.assertTrue(tf.contrib.framework.is_non_decreasing([1]).eval())
  
    def test_empty_tensor_is_non_decreasing(self):
      with self.test_session():
        self.assertTrue(tf.contrib.framework.is_non_decreasing([]).eval())
  
  
  class LocalVariabletest(tf.test.TestCase):
  
    def test_local_variable(self):
      with self.test_session() as sess:
        self.assertEquals([], tf.local_variables())
        value0 = 42
        tf.contrib.framework.local_variable(value0)
        value1 = 43
        tf.contrib.framework.local_variable(value1)
        variables = tf.local_variables()
        self.assertEquals(2, len(variables))
        self.assertRaises(tf.OpError, sess.run, variables)
        tf.initialize_variables(variables).run()
        self.assertAllEqual(set([value0, value1]), set(sess.run(variables)))
  
  
  class ReduceSumNTest(tf.test.TestCase):
  
    def test_reduce_sum_n(self):
      with self.test_session():
        a = tf.constant(1)
        b = tf.constant([2])
        c = tf.constant([[3, 4], [5, 6]])
        self.assertEqual(21, tf.contrib.framework.reduce_sum_n([a, b, c]).eval())
  
  
  class WithShapeTest(tf.test.TestCase):
  
    def _assert_with_shape(
        self, tensor, expected_value, expected_shape, unexpected_shapes):
      for unexpected_shape in unexpected_shapes:
        self.assertRaises(
            ValueError, tf.contrib.framework.with_shape, unexpected_shape, tensor)
        pattern = (
            r"\[Wrong shape for %s \[expected\] \[actual\].\] \[%s\] \[%s\]" %
            (tensor.name,
             " ".join([str(dim) for dim in unexpected_shape]),
             " ".join([str(dim) for dim in expected_shape])))
        self.assertRaisesRegexp(
            tf.OpError,
            re.compile(pattern),
            tf.contrib.framework.with_shape(
                tf.constant(unexpected_shape), tensor).eval)
        expected_placeholder = tf.placeholder(tf.float32)
        self.assertRaisesRegexp(
            tf.OpError,
            re.compile(pattern),
            tf.contrib.framework.with_same_shape(
                expected_placeholder, tensor).eval, {
                    expected_placeholder: np.ones(unexpected_shape)
                })
  
      self.assertIs(tensor, tf.contrib.framework.with_shape(
          expected_shape, tensor))
      self.assertIs(tensor, tf.contrib.framework.with_same_shape(
          tf.constant(1, shape=expected_shape), tensor))
      tensor_with_shape = tf.contrib.framework.with_shape(
          tf.constant(expected_shape), tensor)
      np.testing.assert_array_equal(expected_value, tensor_with_shape.eval())
      tensor_with_same_shape = tf.contrib.framework.with_same_shape(
          expected_placeholder, tensor)
      np.testing.assert_array_equal(expected_value, tensor_with_same_shape.eval({
          expected_placeholder: np.ones(expected_shape)
      }))
  
    def test_with_shape_invalid_expected_shape(self):
      with self.test_session():
        self.assertRaisesRegexp(
            ValueError, "Invalid rank", tf.contrib.framework.with_shape,
            [[1], [2]], tf.constant(1.0))
  
    def test_with_shape_invalid_type(self):
      with self.test_session():
        self.assertRaisesRegexp(
            ValueError, "Invalid dtype", tf.contrib.framework.with_shape,
            [1.1], tf.constant([1.0]))
        self.assertRaisesRegexp(
            ValueError, "Invalid dtype", tf.contrib.framework.with_shape,
            np.array([1.1]), tf.constant(1.0))
        self.assertRaisesRegexp(
            ValueError, "Invalid dtype", tf.contrib.framework.with_shape,
            tf.constant(np.array([1.1])), tf.constant(1.0))
  
    def test_with_shape_0(self):
      with self.test_session():
        value = 42
        shape = [0]
        unexpected_shapes = [[1], [2], [1, 1]]
        self._assert_with_shape(
            tf.constant(value, shape=shape), value, shape, unexpected_shapes)
  
    def test_with_shape_1(self):
      with self.test_session():
        value = [42]
        shape = [1]
        unexpected_shapes = [[0], [2], [1, 1]]
        self._assert_with_shape(
            tf.constant(value, shape=shape), value, shape, unexpected_shapes)
  
    def test_with_shape_2(self):
      with self.test_session():
        value = [42, 43]
        shape = [2]
        unexpected_shapes = [[0], [1], [2, 1]]
        self._assert_with_shape(
            tf.constant(value, shape=shape), value, shape, unexpected_shapes)
  
    def test_with_shape_2x2(self):
      with self.test_session():
        value = [[42, 43], [44, 45]]
        shape = [2, 2]
        unexpected_shapes = [[0], [1], [2, 1]]
        self._assert_with_shape(
            tf.constant(value, shape=shape), value, shape, unexpected_shapes)
  
    def test_with_shape_none(self):
      with self.test_session():
        tensor_no_shape = tf.placeholder(tf.float32)
  
        compatible_shape = [2, 2]
        with_present_2x2 = tf.contrib.framework.with_shape(
            compatible_shape, tensor_no_shape)
        self.assertEquals(compatible_shape, with_present_2x2.get_shape().dims)
        with_future_2x2 = tf.contrib.framework.with_shape(
            tf.constant(compatible_shape), tensor_no_shape)
  
        array_2x2 = [[42.0, 43.0], [44.0, 45.0]]
        for tensor_2x2 in [with_present_2x2, with_future_2x2]:
          np.testing.assert_array_equal(
              array_2x2, tensor_2x2.eval({tensor_no_shape: array_2x2}))
          self.assertRaisesRegexp(
              tf.OpError, "Wrong shape", tensor_2x2.eval,
              {tensor_no_shape: [42.0, 43.0]})
          self.assertRaisesRegexp(
              tf.OpError, "Wrong shape", tensor_2x2.eval,
              {tensor_no_shape: [42.0]})
  
    def test_with_shape_partial(self):
      with self.test_session():
        tensor_partial_shape = tf.placeholder(tf.float32)
        tensor_partial_shape.set_shape([None, 2])
  
        for incompatible_shape in [[0], [1]]:
          self.assertRaisesRegexp(
              ValueError, "must have the same rank",
              tf.contrib.framework.with_shape,
              incompatible_shape, tensor_partial_shape)
        for incompatible_shape in [[1, 2, 1]]:
          self.assertRaisesRegexp(
              ValueError, "Incompatible shapes", tf.contrib.framework.with_shape,
              incompatible_shape, tensor_partial_shape)
        for incompatible_shape in [[2, 1]]:
          self.assertRaisesRegexp(
              ValueError, "Dimensions.*are not compatible",
              tf.contrib.framework.with_shape,
              incompatible_shape, tensor_partial_shape)
  
        compatible_shape = [2, 2]
        with_present_2x2 = tf.contrib.framework.with_shape(
            compatible_shape, tensor_partial_shape)
        self.assertEquals(compatible_shape, with_present_2x2.get_shape().dims)
        with_future_2x2 = tf.contrib.framework.with_shape(
            tf.constant(compatible_shape), tensor_partial_shape)
  
        array_2x2 = [[42.0, 43.0], [44.0, 45.0]]
        for tensor_2x2 in [with_present_2x2, with_future_2x2]:
          np.testing.assert_array_equal(
              array_2x2, tensor_2x2.eval({tensor_partial_shape: array_2x2}))
          self.assertRaises(
              ValueError, tensor_2x2.eval, {tensor_partial_shape: [42.0, 43.0]})
          self.assertRaises(
              ValueError, tensor_2x2.eval, {tensor_partial_shape: [42.0]})
  
  
  if __name__ == "__main__":
    tf.test.main()
  