# Copyright 2017 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import functools

import numpy as np

from tensorflow.python import pywrap_tensorflow
from tensorflow.python.eager import backprop
from tensorflow.python.eager import context
from tensorflow.python.eager import custom_gradient
from tensorflow.python.eager import tape
from tensorflow.python.eager import test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_grad  # pylint: disable=unused-import
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.training import training


class BackpropTest(test.TestCase):

  def testAggregateGradients(self):

    def fn(x):
      ind1 = constant_op.constant(np.array([0, 1]))
      ind2 = constant_op.constant(np.array([2, 3]))
      ind3 = constant_op.constant(np.array([1, 3]))
      # A mixture of IndexedSlices and dense tensor to aggregate.
      g1 = embedding_ops.embedding_lookup(x, ind1)
      g2 = embedding_ops.embedding_lookup(x, ind2)
      g3 = embedding_ops.embedding_lookup(x, ind3)
      g4 = math_ops.reduce_sum(x * constant_op.constant(2.0))
      return g1 * g2 * g3 * g4

    var_np = np.random.rand(4, 2).astype(np.float32)
    var = constant_op.constant(var_np)
    grad = backprop.gradients_function(fn, [0])(var)[0]
    grad = ops.convert_to_tensor(grad).numpy()

    with context.graph_mode(), self.test_session():
      tf_var = array_ops.constant(var_np, dtypes.float32)
      tf_ind1 = array_ops.constant([0, 1])
      tf_ind2 = array_ops.constant([2, 3])