text: # Copyright 2015 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.clip_ops."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  
  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.ops import array_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.platform import test
  
  
  class ClipTest(test.TestCase):
  
    def DISABLED_testClipByValueGradient(self):
      inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32)
      outputs_1 = clip_ops.clip_by_value(inputs, 0.5, 3.5)
      min_val = constant_op.constant([0.5, 0.5, 0.5, 0.5], dtype=dtypes.float32)
      max_val = constant_op.constant([3.5, 3.5, 3.5, 3.5], dtype=dtypes.float32)
      outputs_2 = clip_ops.clip_by_value(inputs, min_val, max_val)
      with self.test_session():
        error_1 = gradient_checker.compute_gradient_error(inputs, [4], outputs_1,
                                                          [4])
        self.assertLess(error_1, 1e-4)
  
        error_2 = gradient_checker.compute_gradient_error(inputs, [4], outputs_2,
                                                          [4])
        self.assertLess(error_2, 1e-4)
  
    # ClipByValue test
    def testClipByValue(self):
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-5.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
        np_ans = [[-4.4, 2.0, 3.0], [4.0, 4.4, 4.4]]
        clip_value = 4.4
        ans = clip_ops.clip_by_value(x, -clip_value, clip_value)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    # [Tensor, Scalar, Scalar]
    def DISABLED_testClipByValue0Type(self):
      for dtype in [
          dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int8,
          dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16
      ]:
        with self.test_session(use_gpu=True):
          x = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=dtype)
          np_ans = [[2, 2, 3], [4, 4, 4]]
          clip_value_min = 2
          clip_value_max = 4
          ans = clip_ops.clip_by_value(x, clip_value_min, clip_value_max)
          tf_ans = ans.eval()
  
        self.assertAllClose(np_ans, tf_ans)
  
    # [Tensor, Tensor, Scalar]
    def DISABLED_testClipByValue1Type(self):
      for dtype in [
          dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int8,
          dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16
      ]:
        with self.test_session(use_gpu=True):
          x = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=dtype)
          np_ans = [[2, 2, 3], [4, 4, 4]]
          clip_value_min = constant_op.constant(
              [2, 2, 2, 3, 3, 3], shape=[2, 3], dtype=dtype)
          clip_value_max = 4
          ans = clip_ops.clip_by_value(x, clip_value_min, clip_value_max)
          tf_ans = ans.eval()
  
        self.assertAllClose(np_ans, tf_ans)
  
    # [Tensor, Scalar, Tensor]
    def DISABLED_testClipByValue2Type(self):
      for dtype in [
          dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int8,
          dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16
      ]:
        with self.test_session(use_gpu=True):
          x = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=dtype)
          np_ans = [[4, 4, 4], [4, 5, 6]]
          clip_value_min = 4
          clip_value_max = constant_op.constant(
              [6, 6, 6, 6, 6, 6], shape=[2, 3], dtype=dtype)
          ans = clip_ops.clip_by_value(x, clip_value_min, clip_value_max)
          tf_ans = ans.eval()
  
        self.assertAllClose(np_ans, tf_ans)
  
    # [Tensor, Tensor, Tensor]
    def DISABLED_testClipByValue3Type(self):
      for dtype in [
          dtypes.float16, dtypes.float32, dtypes.float64, dtypes.int8,
          dtypes.int16, dtypes.int32, dtypes.int64, dtypes.uint8, dtypes.uint16
      ]:
        with self.test_session(use_gpu=True):
          x = constant_op.constant([1, 2, 3, 4, 5, 6], shape=[2, 3], dtype=dtype)
          np_ans = [[2, 2, 3], [5, 5, 6]]
          clip_value_min = constant_op.constant(
              [2, 2, 2, 5, 5, 5], shape=[2, 3], dtype=dtype)
          clip_value_max = constant_op.constant(
              [5, 5, 5, 7, 7, 7], shape=[2, 3], dtype=dtype)
          ans = clip_ops.clip_by_value(x, clip_value_min, clip_value_max)
          tf_ans = ans.eval()
  
        self.assertAllClose(np_ans, tf_ans)
  
    def testClipByValueBadShape(self):
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-5.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3, 1])
        # Use a nonsensical shape.
        clip = constant_op.constant([1.0, 2.0])
        with self.assertRaises(ValueError):
          _ = clip_ops.clip_by_value(x, -clip, clip)
        with self.assertRaises(ValueError):
          _ = clip_ops.clip_by_value(x, 1.0, clip)
  
    def testClipByValueNonFinite(self):
      # TODO(b/78016351): Enable test on GPU once the bug is fixed.
      with self.test_session():
        x = constant_op.constant([float('NaN'), float('Inf'), -float('Inf')])
        np_ans = [float('NaN'), 4.0, -4.0]
        clip_value = 4.0
        ans = clip_ops.clip_by_value(x, -clip_value, clip_value)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    # ClipByNorm tests
    def testClipByNormClipped(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        # Norm of x = sqrt(3^2 + 4^2) = 5
        np_ans = [[-2.4, 0.0, 0.0], [3.2, 0.0, 0.0]]
        clip_norm = 4.0
        ans = clip_ops.clip_by_norm(x, clip_norm)
        tf_ans = ans.eval()
  
        clip_tensor = constant_op.constant(4.0)
        ans = clip_ops.clip_by_norm(x, clip_norm)
        tf_ans_tensor = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
      self.assertAllClose(np_ans, tf_ans_tensor)
  
    def testClipByNormBadShape(self):
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3, 1])
        # Use a nonsensical shape.
        clip = constant_op.constant([1.0, 2.0])
        with self.assertRaises(ValueError):
          _ = clip_ops.clip_by_norm(x, clip)
  
    def testClipByNormNotClipped(self):
      # No norm clipping when clip_norm >= 5
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        # Norm of x = sqrt(3^2 + 4^2) = 5
        np_ans = [[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]]
        clip_norm = 6.0
        ans = clip_ops.clip_by_norm(x, clip_norm)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByNormZero(self):
      # No norm clipping when norm = 0
      with self.test_session(use_gpu=True):
        x = constant_op.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
        # Norm = 0, no changes
        np_ans = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
        clip_norm = 6.0
        ans = clip_ops.clip_by_norm(x, clip_norm)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByNormClippedWithDim0(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 3.0], shape=[2, 3])
        # Norm of x[:, 0] = sqrt(3^2 + 4^2) = 5, x[:, 2] = 3
        np_ans = [[-2.4, 0.0, 0.0], [3.2, 0.0, 3.0]]
        clip_norm = 4.0
        ans = clip_ops.clip_by_norm(x, clip_norm, [0])
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByNormClippedWithDim1(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 3.0], shape=[2, 3])
        # Norm of x[0, :] = 3, x[1, :] = sqrt(3^2 + 4^2) = 5
        np_ans = [[-3.0, 0.0, 0.0], [3.2, 0.0, 2.4]]
        clip_norm = 4.0
        ans = clip_ops.clip_by_norm(x, clip_norm, [1])
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByNormNotClippedWithAxes(self):
      # No norm clipping when clip_norm >= 5
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 3.0], shape=[2, 3])
        # Norm of x[0, :] = 3, x[1, :] = sqrt(3^2 + 4^2) = 5
        np_ans = [[-3.0, 0.0, 0.0], [4.0, 0.0, 3.0]]
        clip_norm = 6.0
        ans = clip_ops.clip_by_norm(x, clip_norm, [1])
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    # ClipByGlobalNorm tests
    def testClipByGlobalNormClipped(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        x1 = constant_op.constant([1.0, -2.0])
        # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
        clip_norm = 4.0
  
        # Answers are the original tensors scaled by 4.0/5.0
        np_ans_0 = [[-1.6, 0.0, 0.0], [3.2, 0.0, 0.0]]
        np_ans_1 = [0.8, -1.6]
  
        ans, norm = clip_ops.clip_by_global_norm((x0, x1), clip_norm)
        tf_ans_1 = ans[0].eval()
        tf_ans_2 = ans[1].eval()
        tf_norm = norm.eval()
  
      self.assertAllClose(tf_norm, 5.0)
      self.assertAllClose(np_ans_0, tf_ans_1)
      self.assertAllClose(np_ans_1, tf_ans_2)
  
    def testClipByGlobalNormClippedTensor(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        x1 = constant_op.constant([1.0, -2.0])
        # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
        clip_norm = constant_op.constant(4.0)
  
        # Answers are the original tensors scaled by 4.0/5.0
        np_ans_0 = [[-1.6, 0.0, 0.0], [3.2, 0.0, 0.0]]
        np_ans_1 = [0.8, -1.6]
  
        ans, norm = clip_ops.clip_by_global_norm((x0, x1), clip_norm)
        tf_ans_1 = ans[0].eval()
        tf_ans_2 = ans[1].eval()
        tf_norm = norm.eval()
  
      self.assertAllClose(tf_norm, 5.0)
      self.assertAllClose(np_ans_0, tf_ans_1)
      self.assertAllClose(np_ans_1, tf_ans_2)
  
    def testClipByGlobalNormSupportsNone(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        x1 = constant_op.constant([1.0, -2.0])
        # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
        clip_norm = 4.0
  
        # Answers are the original tensors scaled by 4.0/5.0
        np_ans_0 = [[-1.6, 0.0, 0.0], [3.2, 0.0, 0.0]]
        np_ans_1 = [0.8, -1.6]
  
        ans, norm = clip_ops.clip_by_global_norm((x0, None, x1, None), clip_norm)
        self.assertTrue(ans[1] is None)
        self.assertTrue(ans[3] is None)
        tf_ans_1 = ans[0].eval()
        tf_ans_2 = ans[2].eval()
        tf_norm = norm.eval()
  
      self.assertAllClose(tf_norm, 5.0)
      self.assertAllClose(np_ans_0, tf_ans_1)
      self.assertAllClose(np_ans_1, tf_ans_2)
  
    def testClipByGlobalNormWithIndexedSlicesClipped(self):
      # Norm clipping when clip_norm < 5
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        x1 = ops.IndexedSlices(
            constant_op.constant([1.0, -2.0]), constant_op.constant([3, 4]))
        # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
        clip_norm = 4.0
  
        # Answers are the original tensors scaled by 4.0/5.0
        np_ans_0 = [[-1.6, 0.0, 0.0], [3.2, 0.0, 0.0]]
        np_ans_1 = [0.8, -1.6]
  
        ans, norm = clip_ops.clip_by_global_norm([x0, x1], clip_norm)
        tf_ans_1 = ans[0].eval()
        tf_ans_2 = ans[1].values.eval()
        tf_norm = norm.eval()
  
      self.assertAllClose(tf_norm, 5.0)
      self.assertAllClose(np_ans_0, tf_ans_1)
      self.assertAllClose(np_ans_1, tf_ans_2)
  
    def testClipByGlobalNormPreservesDenseShape(self):
      dense_shape = (1,)
      slices = ops.IndexedSlices(
          constant_op.constant([1.0]),
          constant_op.constant([0]),
          dense_shape=dense_shape)
      ans, _ = clip_ops.clip_by_global_norm([slices], 1.0)
      modified_slices = ans[0]
      self.assertEqual(dense_shape, slices.dense_shape)
      self.assertEqual(dense_shape, modified_slices.dense_shape)
  
    def testClipByGlobalNormNotClipped(self):
      # No norm clipping when clip_norm >= 5
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([-2.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        x1 = constant_op.constant([1.0, -2.0])
        # Global norm of x0 and x1 = sqrt(1 + 4^2 + 2^2 + 2^2) = 5
        np_ans_0 = [[-2.0, 0.0, 0.0], [4.0, 0.0, 0.0]]
        np_ans_1 = [1.0, -2.0]
        clip_norm = 6.0
  
        ans, norm = clip_ops.clip_by_global_norm([x0, x1], clip_norm)
        tf_ans_1 = ans[0].eval()
        tf_ans_2 = ans[1].eval()
        tf_norm = norm.eval()
  
      self.assertAllClose(tf_norm, 5.0)
      self.assertAllClose(np_ans_0, tf_ans_1)
      self.assertAllClose(np_ans_1, tf_ans_2)
  
    def testClipByGlobalNormZero(self):
      # No norm clipping when norm = 0
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
        x1 = constant_op.constant([0.0, 0.0])
        # Norm = 0, no changes
        np_ans_0 = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
        np_ans_1 = [0.0, 0.0]
        clip_norm = 6.0
  
        ans, norm = clip_ops.clip_by_global_norm([x0, x1], clip_norm)
        tf_ans_1 = ans[0].eval()
        tf_ans_2 = ans[1].eval()
        tf_norm = norm.eval()
  
      self.assertAllClose(tf_norm, 0.0)
      self.assertAllClose(np_ans_0, tf_ans_1)
      self.assertAllClose(np_ans_1, tf_ans_2)
  
    def testClipByGlobalNormInf(self):
      with self.test_session(use_gpu=True):
        x0 = constant_op.constant([-2.0, 0.0, np.inf, 4.0, 0.0, 0.0],
                                  shape=[2, 3])
        x1 = constant_op.constant([1.0, -2.0])
        clip_norm = 6.0
  
        ans, norm = clip_ops.clip_by_global_norm([x0, x1], clip_norm)
        with self.assertRaisesRegexp(errors.InvalidArgumentError, "global norm"):
          norm.eval()
        with self.assertRaisesRegexp(errors.InvalidArgumentError, "global norm"):
          ans[0].eval()
        with self.assertRaisesRegexp(errors.InvalidArgumentError, "global norm"):
          ans[1].eval()
  
    def testClipByAverageNormClipped(self):
      # Norm clipping when average clip_norm < 0.83333333
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
        np_ans = [[-2.88, 0.0, 0.0], [3.84, 0.0, 0.0]]
        clip_norm = 0.8
        ans = clip_ops.clip_by_average_norm(x, clip_norm)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByAverageNormClippedTensor(self):
      # Norm clipping when average clip_norm < 0.83333333
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
        np_ans = [[-2.88, 0.0, 0.0], [3.84, 0.0, 0.0]]
        clip_norm = constant_op.constant(0.8)
        ans = clip_ops.clip_by_average_norm(x, clip_norm)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByAverageNormNotClipped(self):
      # No norm clipping when average clip_norm >= 0.83333333
      with self.test_session(use_gpu=True):
        x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
        # Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
        np_ans = [[-3.0, 0.0, 0.0], [4.0, 0.0, 0.0]]
        clip_norm = 0.9
        ans = clip_ops.clip_by_average_norm(x, clip_norm)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByAverageNormZero(self):
      # No norm clipping when average clip_norm = 0
      with self.test_session(use_gpu=True):
        x = constant_op.constant([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], shape=[2, 3])
        # Average norm = 0, no changes
        np_ans = [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
        clip_norm = 0.9
        ans = clip_ops.clip_by_average_norm(x, clip_norm)
        tf_ans = ans.eval()
  
      self.assertAllClose(np_ans, tf_ans)
  
    def testClipByValueEmptyTensor(self):
      # Test case for GitHub issue 19337
      zero = array_ops.placeholder(dtype=dtypes.float32, shape=None)
      x = clip_ops.clip_by_value(zero, zero, zero)
      y = clip_ops.clip_by_value(zero, 1.0, 1.0)
      z = clip_ops.clip_by_value(zero, zero, 1.0)
      w = clip_ops.clip_by_value(zero, 1.0, zero)
      with self.test_session(use_gpu=True) as sess:
        sess.run([x, y, z, w], feed_dict={zero: np.zeros((7, 0))})
  
  
  if __name__ == '__main__':
    test.main()
  