text: # 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
  # limitations under the License.
  # ==============================================================================
  """Tests for tensorflow.ops.check_ops."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import check_ops
  from tensorflow.python.platform import test
  
  
  class AssertProperIterableTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_single_tensor_raises(self):
      tensor = constant_op.constant(1)
      with self.assertRaisesRegexp(TypeError, "proper"):
        check_ops.assert_proper_iterable(tensor)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_single_sparse_tensor_raises(self):
      ten = sparse_tensor.SparseTensor(
          indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
      with self.assertRaisesRegexp(TypeError, "proper"):
        check_ops.assert_proper_iterable(ten)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_single_ndarray_raises(self):
      array = np.array([1, 2, 3])
      with self.assertRaisesRegexp(TypeError, "proper"):
        check_ops.assert_proper_iterable(array)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_single_string_raises(self):
      mystr = "hello"
      with self.assertRaisesRegexp(TypeError, "proper"):
        check_ops.assert_proper_iterable(mystr)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_non_iterable_object_raises(self):
      non_iterable = 1234
      with self.assertRaisesRegexp(TypeError, "to be iterable"):
        check_ops.assert_proper_iterable(non_iterable)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_list_does_not_raise(self):
      list_of_stuff = [
          constant_op.constant([11, 22]), constant_op.constant([1, 2])
      ]
      check_ops.assert_proper_iterable(list_of_stuff)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_generator_does_not_raise(self):
      generator_of_stuff = (constant_op.constant([11, 22]), constant_op.constant(
          [1, 2]))
      check_ops.assert_proper_iterable(generator_of_stuff)
  
  
  class AssertEqualTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_equal(self):
      small = constant_op.constant([1, 2], name="small")
      with ops.control_dependencies([check_ops.assert_equal(small, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    def test_returns_none_with_eager(self):
      with context.eager_mode():
        small = constant_op.constant([1, 2], name="small")
        x = check_ops.assert_equal(small, small)
        assert x is None
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_greater(self):
      # Static check
      static_small = constant_op.constant([1, 2], name="small")
      static_big = constant_op.constant([3, 4], name="big")
      with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"):
        check_ops.assert_equal(static_big, static_small, message="fail")
  
      # Dynamic check
      if context.in_graph_mode():
        with self.test_session():
          small = array_ops.placeholder(dtypes.int32, name="small")
          big = array_ops.placeholder(dtypes.int32, name="big")
          with ops.control_dependencies(
              [check_ops.assert_equal(
                  big, small, message="fail")]):
            out = array_ops.identity(small)
          with self.assertRaisesOpError("fail.*big.*small"):
            out.eval(feed_dict={small: [1, 2], big: [3, 4]})
  
    def test_error_message_eager(self):
      expected_error_msg_full = r"""big does not equal small
  Condition x == y did not hold.
  Indices of first 3 different values:
  \[\[0 0\]
   \[1 1\]
   \[2 0\]\]
  Corresponding x values:
  \[2 3 6\]
  Corresponding y values:
  \[20 30 60\]
  First 6 elements of x:
  \[2 2 3 3 6 6\]
  First 6 elements of y:
  \[20  2  3 30 60  6\]
  """
      expected_error_msg_default = r"""big does not equal small
  Condition x == y did not hold.
  Indices of first 3 different values:
  \[\[0 0\]
   \[1 1\]
   \[2 0\]\]
  Corresponding x values:
  \[2 3 6\]
  Corresponding y values:
  \[20 30 60\]
  First 3 elements of x:
  \[2 2 3\]
  First 3 elements of y:
  \[20  2  3\]
  """
      expected_error_msg_short = r"""big does not equal small
  Condition x == y did not hold.
  Indices of first 2 different values:
  \[\[0 0\]
   \[1 1\]\]
  Corresponding x values:
  \[2 3\]
  Corresponding y values:
  \[20 30\]
  First 2 elements of x:
  \[2 2\]
  First 2 elements of y:
  \[20  2\]
  """
      with context.eager_mode():
        big = constant_op.constant([[2, 2], [3, 3], [6, 6]])
        small = constant_op.constant([[20, 2], [3, 30], [60, 6]])
        with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                     expected_error_msg_full):
          check_ops.assert_equal(big, small, message="big does not equal small",
                                 summarize=10)
        with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                     expected_error_msg_default):
          check_ops.assert_equal(big, small, message="big does not equal small")
        with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                     expected_error_msg_short):
          check_ops.assert_equal(big, small, message="big does not equal small",
                                 summarize=2)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_less(self):
      # Static check
      static_small = constant_op.constant([3, 1], name="small")
      static_big = constant_op.constant([4, 2], name="big")
      with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"):
        check_ops.assert_equal(static_big, static_small, message="fail")
  
      # Dynamic check
      if context.in_graph_mode():
        with self.test_session():
          small = array_ops.placeholder(dtypes.int32, name="small")
          big = array_ops.placeholder(dtypes.int32, name="big")
          with ops.control_dependencies([check_ops.assert_equal(small, big)]):
            out = array_ops.identity(small)
          with self.assertRaisesOpError("small.*big"):
            out.eval(feed_dict={small: [3, 1], big: [4, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_equal_and_broadcastable_shapes(self):
      small = constant_op.constant([[1, 2], [1, 2]], name="small")
      small_2 = constant_op.constant([1, 2], name="small_2")
      with ops.control_dependencies([check_ops.assert_equal(small, small_2)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_equal_but_non_broadcastable_shapes(self):
      small = constant_op.constant([1, 1, 1], name="small")
      small_2 = constant_op.constant([1, 1], name="small_2")
      # The exception in eager and non-eager mode is different because
      # eager mode relies on shape check done as part of the C++ op, while
      # graph mode does shape checks when creating the `Operation` instance.
      with self.assertRaisesRegexp(
          (errors.InvalidArgumentError, ValueError),
          (r"Incompatible shapes: \[3\] vs. \[2\]|"
           r"Dimensions must be equal, but are 3 and 2")):
        with ops.control_dependencies([check_ops.assert_equal(small, small_2)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      larry = constant_op.constant([])
      curly = constant_op.constant([])
      with ops.control_dependencies([check_ops.assert_equal(larry, curly)]):
        out = array_ops.identity(larry)
      self.evaluate(out)
  
  
  class AssertNoneEqualTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_not_equal(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([10, 20], name="small")
      with ops.control_dependencies(
          [check_ops.assert_none_equal(big, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_equal(self):
      small = constant_op.constant([3, 1], name="small")
      with self.assertRaisesOpError("x != y did not hold"):
        with ops.control_dependencies(
            [check_ops.assert_none_equal(small, small)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_not_equal_and_broadcastable_shapes(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3], name="big")
      with ops.control_dependencies(
          [check_ops.assert_none_equal(small, big)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_not_equal_but_non_broadcastable_shapes(self):
      with self.test_session():
        small = constant_op.constant([1, 1, 1], name="small")
        big = constant_op.constant([10, 10], name="big")
        # The exception in eager and non-eager mode is different because
        # eager mode relies on shape check done as part of the C++ op, while
        # graph mode does shape checks when creating the `Operation` instance.
        with self.assertRaisesRegexp(
            (ValueError, errors.InvalidArgumentError),
            (r"Incompatible shapes: \[3\] vs. \[2\]|"
             r"Dimensions must be equal, but are 3 and 2")):
          with ops.control_dependencies(
              [check_ops.assert_none_equal(small, big)]):
            out = array_ops.identity(small)
          self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      with self.test_session():
        larry = constant_op.constant([])
        curly = constant_op.constant([])
        with ops.control_dependencies(
            [check_ops.assert_none_equal(larry, curly)]):
          out = array_ops.identity(larry)
        self.evaluate(out)
  
    def test_returns_none_with_eager(self):
      with context.eager_mode():
        t1 = constant_op.constant([1, 2])
        t2 = constant_op.constant([3, 4])
        x = check_ops.assert_none_equal(t1, t2)
        assert x is None
  
  
  class AssertAllCloseTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_equal(self):
      x = constant_op.constant(1., name="x")
      y = constant_op.constant(1., name="y")
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_close_enough_32_bit_due_to_default_rtol(self):
      eps = np.finfo(np.float32).eps
      # Default rtol/atol is 10*eps
      x = constant_op.constant(1., name="x")
      y = constant_op.constant(1. + 2 * eps, name="y", dtype=np.float32)
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, atol=0., message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_close_enough_32_bit_due_to_default_atol(self):
      eps = np.finfo(np.float32).eps
      # Default rtol/atol is 10*eps
      x = constant_op.constant(0., name="x")
      y = constant_op.constant(0. + 2 * eps, name="y", dtype=np.float32)
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, rtol=0., message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_close_enough_64_bit_due_to_default_rtol(self):
      eps = np.finfo(np.float64).eps
      # Default rtol/atol is 10*eps
      x = constant_op.constant(1., name="x", dtype=np.float64)
      y = constant_op.constant(1. + 2 * eps, name="y", dtype=np.float64)
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, atol=0., message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_close_enough_64_bit_due_to_default_atol(self):
      eps = np.finfo(np.float64).eps
      # Default rtol/atol is 10*eps
      x = constant_op.constant(0., name="x", dtype=np.float64)
      y = constant_op.constant(0. + 2 * eps, name="y", dtype=np.float64)
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, rtol=0., message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_close_enough_due_to_custom_rtol(self):
      x = constant_op.constant(1., name="x")
      y = constant_op.constant(1.1, name="y")
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, atol=0., rtol=0.5,
                                 message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_close_enough_due_to_custom_atol(self):
      x = constant_op.constant(0., name="x")
      y = constant_op.constant(0.1, name="y", dtype=np.float32)
      with ops.control_dependencies(
          [check_ops.assert_near(x, y, atol=0.5, rtol=0.,
                                 message="failure message")]):
        out = array_ops.identity(x)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      larry = constant_op.constant([])
      curly = constant_op.constant([])
      with ops.control_dependencies([check_ops.assert_near(larry, curly)]):
        out = array_ops.identity(larry)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_atol_violated(self):
      x = constant_op.constant(10., name="x")
      y = constant_op.constant(10.2, name="y")
      with self.assertRaisesOpError("x and y not equal to tolerance"):
        with ops.control_dependencies(
            [check_ops.assert_near(x, y, atol=0.1,
                                   message="failure message")]):
          out = array_ops.identity(x)
          self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_default_rtol_violated(self):
      x = constant_op.constant(0.1, name="x")
      y = constant_op.constant(0.0, name="y")
      with self.assertRaisesOpError("x and y not equal to tolerance"):
        with ops.control_dependencies(
            [check_ops.assert_near(x, y, message="failure message")]):
          out = array_ops.identity(x)
          self.evaluate(out)
  
    def test_returns_none_with_eager(self):
      with context.eager_mode():
        t1 = constant_op.constant([1., 2.])
        t2 = constant_op.constant([1., 2.])
        x = check_ops.assert_near(t1, t2)
        assert x is None
  
  
  class AssertLessTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_equal(self):
      small = constant_op.constant([1, 2], name="small")
      with self.assertRaisesOpError("failure message.*\n*.* x < y did not hold"):
        with ops.control_dependencies(
            [check_ops.assert_less(
                small, small, message="failure message")]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_greater(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3, 4], name="big")
      with self.assertRaisesOpError("x < y did not hold"):
        with ops.control_dependencies([check_ops.assert_less(big, small)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_less(self):
      small = constant_op.constant([3, 1], name="small")
      big = constant_op.constant([4, 2], name="big")
      with ops.control_dependencies([check_ops.assert_less(small, big)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_less_and_broadcastable_shapes(self):
      small = constant_op.constant([1], name="small")
      big = constant_op.constant([3, 2], name="big")
      with ops.control_dependencies([check_ops.assert_less(small, big)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_less_but_non_broadcastable_shapes(self):
      small = constant_op.constant([1, 1, 1], name="small")
      big = constant_op.constant([3, 2], name="big")
      # The exception in eager and non-eager mode is different because
      # eager mode relies on shape check done as part of the C++ op, while
      # graph mode does shape checks when creating the `Operation` instance.
      with self.assertRaisesRegexp(
          (ValueError, errors.InvalidArgumentError),
          (r"Incompatible shapes: \[3\] vs. \[2\]|"
           "Dimensions must be equal, but are 3 and 2")):
        with ops.control_dependencies([check_ops.assert_less(small, big)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      larry = constant_op.constant([])
      curly = constant_op.constant([])
      with ops.control_dependencies([check_ops.assert_less(larry, curly)]):
        out = array_ops.identity(larry)
      self.evaluate(out)
  
    def test_returns_none_with_eager(self):
      with context.eager_mode():
        t1 = constant_op.constant([1, 2])
        t2 = constant_op.constant([3, 4])
        x = check_ops.assert_less(t1, t2)
        assert x is None
  
  
  class AssertLessEqualTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_equal(self):
      small = constant_op.constant([1, 2], name="small")
      with ops.control_dependencies(
          [check_ops.assert_less_equal(small, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_greater(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3, 4], name="big")
      with self.assertRaisesOpError("fail"):
        with ops.control_dependencies(
            [check_ops.assert_less_equal(
                big, small, message="fail")]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_less_equal(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3, 2], name="big")
      with ops.control_dependencies([check_ops.assert_less_equal(small, big)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self):
      small = constant_op.constant([1], name="small")
      big = constant_op.constant([3, 1], name="big")
      with ops.control_dependencies([check_ops.assert_less_equal(small, big)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_less_equal_but_non_broadcastable_shapes(self):
      small = constant_op.constant([3, 1], name="small")
      big = constant_op.constant([1, 1, 1], name="big")
      # The exception in eager and non-eager mode is different because
      # eager mode relies on shape check done as part of the C++ op, while
      # graph mode does shape checks when creating the `Operation` instance.
      with self.assertRaisesRegexp(
          (errors.InvalidArgumentError, ValueError),
          (r"Incompatible shapes: \[2\] vs. \[3\]|"
           r"Dimensions must be equal, but are 2 and 3")):
        with ops.control_dependencies(
            [check_ops.assert_less_equal(small, big)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      larry = constant_op.constant([])
      curly = constant_op.constant([])
      with ops.control_dependencies(
          [check_ops.assert_less_equal(larry, curly)]):
        out = array_ops.identity(larry)
      self.evaluate(out)
  
  
  class AssertGreaterTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_equal(self):
      small = constant_op.constant([1, 2], name="small")
      with self.assertRaisesOpError("fail"):
        with ops.control_dependencies(
            [check_ops.assert_greater(
                small, small, message="fail")]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_less(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3, 4], name="big")
      with self.assertRaisesOpError("x > y did not hold"):
        with ops.control_dependencies([check_ops.assert_greater(small, big)]):
          out = array_ops.identity(big)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_greater(self):
      small = constant_op.constant([3, 1], name="small")
      big = constant_op.constant([4, 2], name="big")
      with ops.control_dependencies([check_ops.assert_greater(big, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_greater_and_broadcastable_shapes(self):
      small = constant_op.constant([1], name="small")
      big = constant_op.constant([3, 2], name="big")
      with ops.control_dependencies([check_ops.assert_greater(big, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_greater_but_non_broadcastable_shapes(self):
      small = constant_op.constant([1, 1, 1], name="small")
      big = constant_op.constant([3, 2], name="big")
      # The exception in eager and non-eager mode is different because
      # eager mode relies on shape check done as part of the C++ op, while
      # graph mode does shape checks when creating the `Operation` instance.
      with self.assertRaisesRegexp(
          (errors.InvalidArgumentError, ValueError),
          (r"Incompatible shapes: \[2\] vs. \[3\]|"
           r"Dimensions must be equal, but are 2 and 3")):
        with ops.control_dependencies([check_ops.assert_greater(big, small)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      larry = constant_op.constant([])
      curly = constant_op.constant([])
      with ops.control_dependencies([check_ops.assert_greater(larry, curly)]):
        out = array_ops.identity(larry)
      self.evaluate(out)
  
  
  class AssertGreaterEqualTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_equal(self):
      small = constant_op.constant([1, 2], name="small")
      with ops.control_dependencies(
          [check_ops.assert_greater_equal(small, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_less(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3, 4], name="big")
      with self.assertRaisesOpError("fail"):
        with ops.control_dependencies(
            [check_ops.assert_greater_equal(
                small, big, message="fail")]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_greater_equal(self):
      small = constant_op.constant([1, 2], name="small")
      big = constant_op.constant([3, 2], name="big")
      with ops.control_dependencies(
          [check_ops.assert_greater_equal(big, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_greater_equal_and_broadcastable_shapes(self):
      small = constant_op.constant([1], name="small")
      big = constant_op.constant([3, 1], name="big")
      with ops.control_dependencies(
          [check_ops.assert_greater_equal(big, small)]):
        out = array_ops.identity(small)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_less_equal_but_non_broadcastable_shapes(self):
      small = constant_op.constant([1, 1, 1], name="big")
      big = constant_op.constant([3, 1], name="small")
      # The exception in eager and non-eager mode is different because
      # eager mode relies on shape check done as part of the C++ op, while
      # graph mode does shape checks when creating the `Operation` instance.
      with self.assertRaisesRegexp(
          (errors.InvalidArgumentError, ValueError),
          (r"Incompatible shapes: \[2\] vs. \[3\]|"
           r"Dimensions must be equal, but are 2 and 3")):
        with ops.control_dependencies(
            [check_ops.assert_greater_equal(big, small)]):
          out = array_ops.identity(small)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_both_empty(self):
      larry = constant_op.constant([])
      curly = constant_op.constant([])
      with ops.control_dependencies(
          [check_ops.assert_greater_equal(larry, curly)]):
        out = array_ops.identity(larry)
      self.evaluate(out)
  
  
  class AssertNegativeTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_negative(self):
      frank = constant_op.constant([-1, -2], name="frank")
      with ops.control_dependencies([check_ops.assert_negative(frank)]):
        out = array_ops.identity(frank)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_positive(self):
      doug = constant_op.constant([1, 2], name="doug")
      with self.assertRaisesOpError("fail"):
        with ops.control_dependencies(
            [check_ops.assert_negative(
                doug, message="fail")]):
          out = array_ops.identity(doug)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_zero(self):
      claire = constant_op.constant([0], name="claire")
      with self.assertRaisesOpError("x < 0 did not hold"):
        with ops.control_dependencies([check_ops.assert_negative(claire)]):
          out = array_ops.identity(claire)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    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.
      empty = constant_op.constant([], name="empty")
      with ops.control_dependencies([check_ops.assert_negative(empty)]):
        out = array_ops.identity(empty)
      self.evaluate(out)
  
  
  class AssertPositiveTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_negative(self):
      freddie = constant_op.constant([-1, -2], name="freddie")
      with self.assertRaisesOpError("fail"):
        with ops.control_dependencies(
            [check_ops.assert_positive(
                freddie, message="fail")]):
          out = array_ops.identity(freddie)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_positive(self):
      remmy = constant_op.constant([1, 2], name="remmy")
      with ops.control_dependencies([check_ops.assert_positive(remmy)]):
        out = array_ops.identity(remmy)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_zero(self):
      meechum = constant_op.constant([0], name="meechum")
      with self.assertRaisesOpError("x > 0 did not hold"):
        with ops.control_dependencies([check_ops.assert_positive(meechum)]):
          out = array_ops.identity(meechum)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    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.
      empty = constant_op.constant([], name="empty")
      with ops.control_dependencies([check_ops.assert_positive(empty)]):
        out = array_ops.identity(empty)
      self.evaluate(out)
  
  
  class AssertRankTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self):
      tensor = constant_op.constant(1, name="my_tensor")
      desired_rank = 1
      with self.assertRaisesRegexp(ValueError,
                                   "fail.*must have rank 1"):
        with ops.control_dependencies(
            [check_ops.assert_rank(
                tensor, desired_rank, message="fail")]):
          self.evaluate(array_ops.identity(tensor))
  
    def test_rank_zero_tensor_raises_if_rank_too_small_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 1
        with ops.control_dependencies(
            [check_ops.assert_rank(
                tensor, desired_rank, message="fail")]):
          with self.assertRaisesOpError("fail.*my_tensor.*rank"):
            array_ops.identity(tensor).eval(feed_dict={tensor: 0})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self):
      tensor = constant_op.constant(1, name="my_tensor")
      desired_rank = 0
      with ops.control_dependencies(
          [check_ops.assert_rank(tensor, desired_rank)]):
        self.evaluate(array_ops.identity(tensor))
  
    def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 0
        with ops.control_dependencies(
            [check_ops.assert_rank(tensor, desired_rank)]):
          array_ops.identity(tensor).eval(feed_dict={tensor: 0})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_raises_if_rank_too_large_static_rank(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      desired_rank = 0
      with self.assertRaisesRegexp(ValueError, "rank"):
        with ops.control_dependencies(
            [check_ops.assert_rank(tensor, desired_rank)]):
          self.evaluate(array_ops.identity(tensor))
  
    def test_rank_one_tensor_raises_if_rank_too_large_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 0
        with ops.control_dependencies(
            [check_ops.assert_rank(tensor, desired_rank)]):
          with self.assertRaisesOpError("my_tensor.*rank"):
            array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      desired_rank = 1
      with ops.control_dependencies(
          [check_ops.assert_rank(tensor, desired_rank)]):
        self.evaluate(array_ops.identity(tensor))
  
    def test_rank_one_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 1
        with ops.control_dependencies(
            [check_ops.assert_rank(tensor, desired_rank)]):
          array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      desired_rank = 2
      with self.assertRaisesRegexp(ValueError, "rank"):
        with ops.control_dependencies(
            [check_ops.assert_rank(tensor, desired_rank)]):
          self.evaluate(array_ops.identity(tensor))
  
    def test_rank_one_tensor_raises_if_rank_too_small_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 2
        with ops.control_dependencies(
            [check_ops.assert_rank(tensor, desired_rank)]):
          with self.assertRaisesOpError("my_tensor.*rank"):
            array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_if_rank_is_not_scalar_static(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"):
        check_ops.assert_rank(tensor, np.array([], dtype=np.int32))
  
    def test_raises_if_rank_is_not_scalar_dynamic(self):
      with self.test_session():
        tensor = constant_op.constant(
            [1, 2], dtype=dtypes.float32, name="my_tensor")
        rank_tensor = array_ops.placeholder(dtypes.int32, name="rank_tensor")
        with self.assertRaisesOpError("Rank must be a scalar"):
          with ops.control_dependencies(
              [check_ops.assert_rank(tensor, rank_tensor)]):
            array_ops.identity(tensor).eval(feed_dict={rank_tensor: [1, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_if_rank_is_not_integer_static(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      with self.assertRaisesRegexp(TypeError,
                                   "must be of type <dtype: 'int32'>"):
        check_ops.assert_rank(tensor, .5)
  
    def test_raises_if_rank_is_not_integer_dynamic(self):
      with self.test_session():
        tensor = constant_op.constant(
            [1, 2], dtype=dtypes.float32, name="my_tensor")
        rank_tensor = array_ops.placeholder(dtypes.float32, name="rank_tensor")
        with self.assertRaisesRegexp(TypeError,
                                     "must be of type <dtype: 'int32'>"):
          with ops.control_dependencies(
              [check_ops.assert_rank(tensor, rank_tensor)]):
            array_ops.identity(tensor).eval(feed_dict={rank_tensor: .5})
  
  
  class AssertRankInTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self):
      tensor_rank0 = constant_op.constant(42, name="my_tensor")
      with self.assertRaisesRegexp(
          ValueError, "fail.*must have rank.*in.*1.*2"):
        with ops.control_dependencies([
            check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]):
          self.evaluate(array_ops.identity(tensor_rank0))
  
    def test_rank_zero_tensor_raises_if_rank_mismatch_dynamic_rank(self):
      with self.test_session():
        tensor_rank0 = array_ops.placeholder(dtypes.float32, name="my_tensor")
        with ops.control_dependencies([
            check_ops.assert_rank_in(tensor_rank0, (1, 2), message="fail")]):
          with self.assertRaisesOpError("fail.*my_tensor.*rank"):
            array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_zero_tensor_doesnt_raise_if_rank_matches_static_rank(self):
      tensor_rank0 = constant_op.constant(42, name="my_tensor")
      for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
        with ops.control_dependencies([
            check_ops.assert_rank_in(tensor_rank0, desired_ranks)]):
          self.evaluate(array_ops.identity(tensor_rank0))
  
    def test_rank_zero_tensor_doesnt_raise_if_rank_matches_dynamic_rank(self):
      with self.test_session():
        tensor_rank0 = array_ops.placeholder(dtypes.float32, name="my_tensor")
        for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
          with ops.control_dependencies([
              check_ops.assert_rank_in(tensor_rank0, desired_ranks)]):
            array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_doesnt_raise_if_rank_matches_static_rank(self):
      tensor_rank1 = constant_op.constant([42, 43], name="my_tensor")
      for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
        with ops.control_dependencies([
            check_ops.assert_rank_in(tensor_rank1, desired_ranks)]):
          self.evaluate(array_ops.identity(tensor_rank1))
  
    def test_rank_one_tensor_doesnt_raise_if_rank_matches_dynamic_rank(self):
      with self.test_session():
        tensor_rank1 = array_ops.placeholder(dtypes.float32, name="my_tensor")
        for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)):
          with ops.control_dependencies([
              check_ops.assert_rank_in(tensor_rank1, desired_ranks)]):
            array_ops.identity(tensor_rank1).eval(feed_dict={
                tensor_rank1: (42.0, 43.0)
            })
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_raises_if_rank_mismatches_static_rank(self):
      tensor_rank1 = constant_op.constant((42, 43), name="my_tensor")
      with self.assertRaisesRegexp(ValueError, "rank"):
        with ops.control_dependencies([
            check_ops.assert_rank_in(tensor_rank1, (0, 2))]):
          self.evaluate(array_ops.identity(tensor_rank1))
  
    def test_rank_one_tensor_raises_if_rank_mismatches_dynamic_rank(self):
      with self.test_session():
        tensor_rank1 = array_ops.placeholder(dtypes.float32, name="my_tensor")
        with ops.control_dependencies([
            check_ops.assert_rank_in(tensor_rank1, (0, 2))]):
          with self.assertRaisesOpError("my_tensor.*rank"):
            array_ops.identity(tensor_rank1).eval(feed_dict={
                tensor_rank1: (42.0, 43.0)
            })
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_if_rank_is_not_scalar_static(self):
      tensor = constant_op.constant((42, 43), name="my_tensor")
      desired_ranks = (
          np.array(1, dtype=np.int32),
          np.array((2, 1), dtype=np.int32))
      with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"):
        check_ops.assert_rank_in(tensor, desired_ranks)
  
    def test_raises_if_rank_is_not_scalar_dynamic(self):
      with self.test_session():
        tensor = constant_op.constant(
            (42, 43), dtype=dtypes.float32, name="my_tensor")
        desired_ranks = (
            array_ops.placeholder(dtypes.int32, name="rank0_tensor"),
            array_ops.placeholder(dtypes.int32, name="rank1_tensor"))
        with self.assertRaisesOpError("Rank must be a scalar"):
          with ops.control_dependencies(
              (check_ops.assert_rank_in(tensor, desired_ranks),)):
            array_ops.identity(tensor).eval(feed_dict={
                desired_ranks[0]: 1,
                desired_ranks[1]: [2, 1],
            })
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_if_rank_is_not_integer_static(self):
      tensor = constant_op.constant((42, 43), name="my_tensor")
      with self.assertRaisesRegexp(TypeError,
                                   "must be of type <dtype: 'int32'>"):
        check_ops.assert_rank_in(tensor, (1, .5,))
  
    def test_raises_if_rank_is_not_integer_dynamic(self):
      with self.test_session():
        tensor = constant_op.constant(
            (42, 43), dtype=dtypes.float32, name="my_tensor")
        rank_tensor = array_ops.placeholder(dtypes.float32, name="rank_tensor")
        with self.assertRaisesRegexp(TypeError,
                                     "must be of type <dtype: 'int32'>"):
          with ops.control_dependencies(
              [check_ops.assert_rank_in(tensor, (1, rank_tensor))]):
            array_ops.identity(tensor).eval(feed_dict={rank_tensor: .5})
  
  
  class AssertRankAtLeastTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self):
      tensor = constant_op.constant(1, name="my_tensor")
      desired_rank = 1
      with self.assertRaisesRegexp(ValueError, "rank at least 1"):
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          self.evaluate(array_ops.identity(tensor))
  
    def test_rank_zero_tensor_raises_if_rank_too_small_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 1
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          with self.assertRaisesOpError("my_tensor.*rank"):
            array_ops.identity(tensor).eval(feed_dict={tensor: 0})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self):
      tensor = constant_op.constant(1, name="my_tensor")
      desired_rank = 0
      with ops.control_dependencies(
          [check_ops.assert_rank_at_least(tensor, desired_rank)]):
        self.evaluate(array_ops.identity(tensor))
  
    def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 0
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          array_ops.identity(tensor).eval(feed_dict={tensor: 0})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_ten_doesnt_raise_raise_if_rank_too_large_static_rank(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      desired_rank = 0
      with ops.control_dependencies(
          [check_ops.assert_rank_at_least(tensor, desired_rank)]):
        self.evaluate(array_ops.identity(tensor))
  
    def test_rank_one_ten_doesnt_raise_if_rank_too_large_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 0
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      desired_rank = 1
      with ops.control_dependencies(
          [check_ops.assert_rank_at_least(tensor, desired_rank)]):
        self.evaluate(array_ops.identity(tensor))
  
    def test_rank_one_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 1
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
  
    @test_util.run_in_graph_and_eager_modes()
    def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self):
      tensor = constant_op.constant([1, 2], name="my_tensor")
      desired_rank = 2
      with self.assertRaisesRegexp(ValueError, "rank at least 2"):
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          self.evaluate(array_ops.identity(tensor))
  
    def test_rank_one_tensor_raises_if_rank_too_small_dynamic_rank(self):
      with self.test_session():
        tensor = array_ops.placeholder(dtypes.float32, name="my_tensor")
        desired_rank = 2
        with ops.control_dependencies(
            [check_ops.assert_rank_at_least(tensor, desired_rank)]):
          with self.assertRaisesOpError("my_tensor.*rank"):
            array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]})
  
  
  class AssertNonNegativeTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_negative(self):
      zoe = constant_op.constant([-1, -2], name="zoe")
      with self.assertRaisesOpError("x >= 0 did not hold"):
        with ops.control_dependencies([check_ops.assert_non_negative(zoe)]):
          out = array_ops.identity(zoe)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_zero_and_positive(self):
      lucas = constant_op.constant([0, 2], name="lucas")
      with ops.control_dependencies([check_ops.assert_non_negative(lucas)]):
        out = array_ops.identity(lucas)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    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.
      empty = constant_op.constant([], name="empty")
      with ops.control_dependencies([check_ops.assert_non_negative(empty)]):
        out = array_ops.identity(empty)
      self.evaluate(out)
  
  
  class AssertNonPositiveTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_zero_and_negative(self):
      tom = constant_op.constant([0, -2], name="tom")
      with ops.control_dependencies([check_ops.assert_non_positive(tom)]):
        out = array_ops.identity(tom)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_positive(self):
      rachel = constant_op.constant([0, 2], name="rachel")
      with self.assertRaisesOpError("x <= 0 did not hold"):
        with ops.control_dependencies([check_ops.assert_non_positive(rachel)]):
          out = array_ops.identity(rachel)
        self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    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.
      empty = constant_op.constant([], name="empty")
      with ops.control_dependencies([check_ops.assert_non_positive(empty)]):
        out = array_ops.identity(empty)
      self.evaluate(out)
  
  
  class AssertIntegerTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_integer(self):
      integers = constant_op.constant([1, 2], name="integers")
      with ops.control_dependencies([check_ops.assert_integer(integers)]):
        out = array_ops.identity(integers)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_float(self):
      floats = constant_op.constant([1.0, 2.0], name="floats")
      with self.assertRaisesRegexp(TypeError, "Expected.*integer"):
        check_ops.assert_integer(floats)
  
  
  class AssertTypeTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_doesnt_raise_when_correct_type(self):
      integers = constant_op.constant([1, 2], dtype=dtypes.int64)
      with ops.control_dependencies([
          check_ops.assert_type(integers, dtypes.int64)]):
        out = array_ops.identity(integers)
      self.evaluate(out)
  
    @test_util.run_in_graph_and_eager_modes()
    def test_raises_when_wrong_type(self):
      floats = constant_op.constant([1.0, 2.0], dtype=dtypes.float16)
      with self.assertRaisesRegexp(TypeError, "must be of type.*float32"):
        check_ops.assert_type(floats, dtypes.float32)
  
  
  class IsStrictlyIncreasingTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_constant_tensor_is_not_strictly_increasing(self):
      self.assertFalse(self.evaluate(check_ops.is_strictly_increasing([1, 1, 1])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_decreasing_tensor_is_not_strictly_increasing(self):
      self.assertFalse(self.evaluate(
          check_ops.is_strictly_increasing([1, 0, -1])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_2d_decreasing_tensor_is_not_strictly_increasing(self):
      self.assertFalse(
          self.evaluate(check_ops.is_strictly_increasing([[1, 3], [2, 4]])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_increasing_tensor_is_increasing(self):
      self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([1, 2, 3])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_increasing_rank_two_tensor(self):
      self.assertTrue(
          self.evaluate(check_ops.is_strictly_increasing([[-1, 2], [3, 4]])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_tensor_with_one_element_is_strictly_increasing(self):
      self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([1])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_empty_tensor_is_strictly_increasing(self):
      self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([])))
  
  
  class IsNonDecreasingTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_constant_tensor_is_non_decreasing(self):
      self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1, 1, 1])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_decreasing_tensor_is_not_non_decreasing(self):
      self.assertFalse(self.evaluate(check_ops.is_non_decreasing([3, 2, 1])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_2d_decreasing_tensor_is_not_non_decreasing(self):
      self.assertFalse(self.evaluate(
          check_ops.is_non_decreasing([[1, 3], [2, 4]])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_increasing_rank_one_tensor_is_non_decreasing(self):
      self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1, 2, 3])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_increasing_rank_two_tensor(self):
      self.assertTrue(self.evaluate(
          check_ops.is_non_decreasing([[-1, 2], [3, 3]])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_tensor_with_one_element_is_non_decreasing(self):
      self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1])))
  
    @test_util.run_in_graph_and_eager_modes()
    def test_empty_tensor_is_non_decreasing(self):
      self.assertTrue(self.evaluate(check_ops.is_non_decreasing([])))
  
  
  class FloatDTypeTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_assert_same_float_dtype(self):
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype(None, None))
      self.assertIs(dtypes.float32, check_ops.assert_same_float_dtype([], None))
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype([], dtypes.float32))
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype(None, dtypes.float32))
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype([None, None], None))
      self.assertIs(
          dtypes.float32,
          check_ops.assert_same_float_dtype([None, None], dtypes.float32))
  
      const_float = constant_op.constant(3.0, dtype=dtypes.float32)
      self.assertIs(
          dtypes.float32,
          check_ops.assert_same_float_dtype([const_float], dtypes.float32))
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [const_float], dtypes.int32)
  
      sparse_float = sparse_tensor.SparseTensor(
          constant_op.constant([[111], [232]], dtypes.int64),
          constant_op.constant([23.4, -43.2], dtypes.float32),
          constant_op.constant([500], dtypes.int64))
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype([sparse_float],
                                                      dtypes.float32))
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [sparse_float], dtypes.int32)
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [const_float, None, sparse_float], dtypes.float64)
  
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype(
                        [const_float, sparse_float]))
      self.assertIs(dtypes.float32,
                    check_ops.assert_same_float_dtype(
                        [const_float, sparse_float], dtypes.float32))
  
      const_int = constant_op.constant(3, dtype=dtypes.int32)
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [sparse_float, const_int])
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [sparse_float, const_int], dtypes.int32)
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [sparse_float, const_int], dtypes.float32)
      self.assertRaises(ValueError, check_ops.assert_same_float_dtype,
                        [const_int])
  
  
  class AssertScalarTest(test.TestCase):
  
    @test_util.run_in_graph_and_eager_modes()
    def test_assert_scalar(self):
      check_ops.assert_scalar(constant_op.constant(3))
      check_ops.assert_scalar(constant_op.constant("foo"))
      check_ops.assert_scalar(3)
      check_ops.assert_scalar("foo")
      with self.assertRaisesRegexp(ValueError, "Expected scalar"):
        check_ops.assert_scalar(constant_op.constant([3, 4]))
  
  
  if __name__ == "__main__":
    test.main()
  