# Copyright 2019 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 AddN."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import list_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test


class XlaAddNTest(xla_test.XLATestCase):

  def testAddTensorLists(self):
    with self.session(), self.test_scope():
      l1 = list_ops.tensor_list_reserve(
          element_shape=[], element_dtype=dtypes.float32, num_elements=3)
      l2 = list_ops.tensor_list_reserve(
          element_shape=[], element_dtype=dtypes.float32, num_elements=3)
      l1 = list_ops.tensor_list_set_item(l1, 0, 5.)
      l2 = list_ops.tensor_list_set_item(l2, 2, 10.)

      l = math_ops.add_n([l1, l2])
      self.assertAllEqual(
          list_ops.tensor_list_stack(l, element_dtype=dtypes.float32),
          [5.0, 0.0, 10.0])

  def testAddTensorListsFailsIfLeadingDimsMismatch(self):
    with self.session(), self.test_scope():
      l1 = list_ops.tensor_list_reserve(
          element_shape=[], element_dtype=dtypes.float32, num_elements=2)
      l2 = list_ops.tensor_list_reserve(
          element_shape=[], element_dtype=dtypes.float32, num_elements=3)
      l = math_ops.add_n([l1, l2])
      with self.assertRaisesRegexp(
          errors.InvalidArgumentError,
          "TensorList arguments to AddN must all have the same shape"):
        list_ops.tensor_list_stack(l, element_dtype=dtypes.float32).eval()

  def testAddTensorListsFailsIfElementShapesMismatch(self):
    with self.session() as session, self.test_scope():
      # Use placeholders instead of constant values for shapes to prevent TF's
      # shape inference from catching this early.
      l1_element_shape = array_ops.placeholder(dtype=dtypes.int32)
      l2_element_shape = array_ops.placeholder(dtype=dtypes.int32)
      l1 = list_ops.tensor_list_reserve(
          element_shape=l1_element_shape,
          element_dtype=dtypes.float32,
          num_elements=3)
      l2 = list_ops.tensor_list_reserve(
          element_shape=l2_element_shape,
          element_dtype=dtypes.float32,
          num_elements=3)
      l = math_ops.add_n([l1, l2])
      with self.assertRaisesRegexp(
          errors.InvalidArgumentError,
          "TensorList arguments to AddN must all have the same shape"):
        session.run(
            list_ops.tensor_list_stack(l, element_dtype=dtypes.float32), {
                l1_element_shape: [],
                l2_element_shape: [2]
            })


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