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.tf.scatter."""
  
  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 test_util
  from tensorflow.python.ops import state_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import test
  
  
  def _AsType(v, vtype):
    return v.astype(vtype) if isinstance(v, np.ndarray) else vtype(v)
  
  
  def _NumpyAdd(ref, indices, updates):
    # Since numpy advanced assignment does not support repeated indices,
    # we run a simple loop to perform scatter_add.
    for i, indx in np.ndenumerate(indices):
      ref[indx] += updates[i]
  
  
  def _NumpyAddScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] += update
  
  
  def _NumpySub(ref, indices, updates):
    for i, indx in np.ndenumerate(indices):
      ref[indx] -= updates[i]
  
  
  def _NumpySubScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] -= update
  
  
  def _NumpyMul(ref, indices, updates):
    for i, indx in np.ndenumerate(indices):
      ref[indx] *= updates[i]
  
  
  def _NumpyMulScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] *= update
  
  
  def _NumpyDiv(ref, indices, updates):
    for i, indx in np.ndenumerate(indices):
      ref[indx] /= updates[i]
  
  
  def _NumpyDivScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] /= update
  
  
  def _NumpyMin(ref, indices, updates):
    for i, indx in np.ndenumerate(indices):
      ref[indx] = np.minimum(ref[indx], updates[i])
  
  
  def _NumpyMinScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] = np.minimum(ref[indx], update)
  
  
  def _NumpyMax(ref, indices, updates):
    for i, indx in np.ndenumerate(indices):
      ref[indx] = np.maximum(ref[indx], updates[i])
  
  
  def _NumpyMaxScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] = np.maximum(ref[indx], update)
  
  
  def _NumpyUpdate(ref, indices, updates):
    for i, indx in np.ndenumerate(indices):
      ref[indx] = updates[i]
  
  
  def _NumpyUpdateScalar(ref, indices, update):
    for _, indx in np.ndenumerate(indices):
      ref[indx] = update
  
  
  _TF_OPS_TO_NUMPY = {
      state_ops.scatter_update: _NumpyUpdate,
      state_ops.scatter_add: _NumpyAdd,
      state_ops.scatter_sub: _NumpySub,
      state_ops.scatter_mul: _NumpyMul,
      state_ops.scatter_div: _NumpyDiv,
      state_ops.scatter_min: _NumpyMin,
      state_ops.scatter_max: _NumpyMax,
  }
  
  _TF_OPS_TO_NUMPY_SCALAR = {
      state_ops.scatter_update: _NumpyUpdateScalar,
      state_ops.scatter_add: _NumpyAddScalar,
      state_ops.scatter_sub: _NumpySubScalar,
      state_ops.scatter_mul: _NumpyMulScalar,
      state_ops.scatter_div: _NumpyDivScalar,
      state_ops.scatter_min: _NumpyMinScalar,
      state_ops.scatter_max: _NumpyMaxScalar,
  }
  
  
  class ScatterTest(test.TestCase):
  
    def _VariableRankTest(self,
                          tf_scatter,
                          vtype,
                          itype,
                          repeat_indices=False,
                          updates_are_scalar=False):
      np.random.seed(8)
      with self.cached_session():
        for indices_shape in (), (2,), (3, 7), (3, 4, 7):
          for extra_shape in (), (5,), (5, 9):
            # Generate random indices with no duplicates for easy numpy comparison
            size = np.prod(indices_shape, dtype=itype)
            first_dim = 3 * size
            indices = np.arange(first_dim)
            np.random.shuffle(indices)
            indices = indices[:size]
            if size > 1 and repeat_indices:
              # Add some random repeats.
              indices = indices[:size // 2]
              for _ in range(size - size // 2):
                # Randomly append some repeats.
                indices = np.append(indices,
                                    indices[np.random.randint(size // 2)])
              np.random.shuffle(indices)
            indices = indices.reshape(indices_shape)
            if updates_are_scalar:
              updates = _AsType(np.random.randn(), vtype)
            else:
              updates = _AsType(
                  np.random.randn(*(indices_shape + extra_shape)), vtype)
  
            # Clips small values to avoid division by zero.
            def clip_small_values(x):
              threshold = 1e-4
              sign = np.sign(x)
  
              if isinstance(x, np.int32):
                threshold = 1
                sign = np.random.choice([-1, 1])
              return threshold * sign if np.abs(x) < threshold else x
  
            updates = np.vectorize(clip_small_values)(updates)
            old = _AsType(np.random.randn(*((first_dim,) + extra_shape)), vtype)
  
            # Scatter via numpy
            new = old.copy()
            if updates_are_scalar:
              np_scatter = _TF_OPS_TO_NUMPY_SCALAR[tf_scatter]
            else:
              np_scatter = _TF_OPS_TO_NUMPY[tf_scatter]
            np_scatter(new, indices, updates)
            # Scatter via tensorflow
            ref = variables.Variable(old)
            self.evaluate(ref.initializer)
            self.evaluate(tf_scatter(ref, indices, updates))
            self.assertAllCloseAccordingToType(
                self.evaluate(ref), new, half_rtol=5e-3, half_atol=5e-3)
  
    def _VariableRankTests(self,
                           tf_scatter,
                           repeat_indices=False,
                           updates_are_scalar=False):
      vtypes = [np.float32, np.float64]
      if tf_scatter != state_ops.scatter_div:
        vtypes.append(np.int32)
        # float16 is numerically unstable for div
        vtypes.append(np.float16)
  
      for vtype in vtypes:
        for itype in (np.int32, np.int64):
          self._VariableRankTest(tf_scatter, vtype, itype, repeat_indices,
                                 updates_are_scalar)
  
    def testVariableRankUpdate(self):
      self._VariableRankTests(state_ops.scatter_update, False)
  
    def testVariableRankAdd(self):
      self._VariableRankTests(state_ops.scatter_add, False)
  
    def testVariableRankSub(self):
      self._VariableRankTests(state_ops.scatter_sub, False)
  
    def testVariableRankMul(self):
      self._VariableRankTests(state_ops.scatter_mul, False)
  
    def testVariableRankDiv(self):
      self._VariableRankTests(state_ops.scatter_div, False)
  
    def testVariableRankMin(self):
      self._VariableRankTests(state_ops.scatter_min, False)
  
    def testVariableRankMax(self):
      self._VariableRankTests(state_ops.scatter_max, False)
  
    def testRepeatIndicesAdd(self):
      self._VariableRankTests(state_ops.scatter_add, True)
  
    def testRepeatIndicesSub(self):
      self._VariableRankTests(state_ops.scatter_sub, True)
  
    def testRepeatIndicesMul(self):
      self._VariableRankTests(state_ops.scatter_mul, True)
  
    def testRepeatIndicesDiv(self):
      self._VariableRankTests(state_ops.scatter_div, True)
  
    def testRepeatIndicesMin(self):
      self._VariableRankTests(state_ops.scatter_min, True)
  
    def testRepeatIndicesMax(self):
      self._VariableRankTests(state_ops.scatter_max, True)
  
    def testVariableRankUpdateScalar(self):
      self._VariableRankTests(state_ops.scatter_update, False, True)
  
    def testVariableRankAddScalar(self):
      self._VariableRankTests(state_ops.scatter_add, False, True)
  
    def testVariableRankSubScalar(self):
      self._VariableRankTests(state_ops.scatter_sub, False, True)
  
    def testVariableRankMulScalar(self):
      self._VariableRankTests(state_ops.scatter_mul, False, True)
  
    def testVariableRankDivScalar(self):
      self._VariableRankTests(state_ops.scatter_div, False, True)
  
    def testVariableRankMinScalar(self):
      self._VariableRankTests(state_ops.scatter_min, False, True)
  
    def testVariableRankMaxScalar(self):
      self._VariableRankTests(state_ops.scatter_max, False, True)
  
    def testRepeatIndicesAddScalar(self):
      self._VariableRankTests(state_ops.scatter_add, True, True)
  
    def testRepeatIndicesSubScalar(self):
      self._VariableRankTests(state_ops.scatter_sub, True, True)
  
    def testRepeatIndicesMulScalar(self):
      self._VariableRankTests(state_ops.scatter_mul, True, True)
  
    def testRepeatIndicesDivScalar(self):
      self._VariableRankTests(state_ops.scatter_div, True, True)
  
    def testRepeatIndicesMinScalar(self):
      self._VariableRankTests(state_ops.scatter_min, True, True)
  
    def testRepeatIndicesMaxScalar(self):
      self._VariableRankTests(state_ops.scatter_max, True, True)
  
    def testBooleanScatterUpdate(self):
      if not test.is_gpu_available():
        with self.session(use_gpu=False):
          var = variables.Variable([True, False])
          update0 = state_ops.scatter_update(var, 1, True)
          update1 = state_ops.scatter_update(
              var, constant_op.constant(
                  0, dtype=dtypes.int64), False)
          self.evaluate(var.initializer)
  
          self.evaluate([update0, update1])
  
          self.assertAllEqual([False, True], self.evaluate(var))
  
    def testScatterOutOfRangeCpu(self):
      for op, _ in _TF_OPS_TO_NUMPY.items():
        params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
        updates = np.array([-3, -4, -5]).astype(np.float32)
        if not test.is_gpu_available():
          with self.session(use_gpu=False):
            ref = variables.Variable(params)
            self.evaluate(ref.initializer)
  
            # Indices all in range, no problem.
            indices = np.array([2, 0, 5])
            self.evaluate(op(ref, indices, updates))
  
            # Test some out of range errors.
            indices = np.array([-1, 0, 5])
            with self.assertRaisesOpError(
                r'indices\[0\] = -1 is not in \[0, 6\)'):
              self.evaluate(op(ref, indices, updates))
  
            indices = np.array([2, 0, 6])
            with self.assertRaisesOpError(r'indices\[2\] = 6 is not in \[0, 6\)'):
              self.evaluate(op(ref, indices, updates))
  
    # TODO(fpmc): Re-enable this test when gpu_pip test actually runs on a GPU.
    def _disabledTestScatterOutOfRangeGpu(self):
      if test.is_gpu_available():
        return
      for op, _ in _TF_OPS_TO_NUMPY.items():
        params = np.array([1, 2, 3, 4, 5, 6]).astype(np.float32)
        updates = np.array([-3, -4, -5]).astype(np.float32)
        # With GPU, the code ignores indices that are out of range.
        # We don't test the implementation; just test there's no failures.
        with test_util.force_gpu():
          ref = variables.Variable(params)
          self.evaluate(ref.initializer)
  
          # Indices all in range, no problem.
          indices = np.array([2, 0, 5])
          self.evaluate(op(ref, indices, updates))
  
          # Indices out of range should not fail.
          indices = np.array([-1, 0, 5])
          self.evaluate(op(ref, indices, updates))
          indices = np.array([2, 0, 6])
          self.evaluate(op(ref, indices, updates))
  
  
  if __name__ == '__main__':
    test.main()
  