# 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.
# ==============================================================================
"""Python wrapper for the Block GRU Op."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.contrib.util import loader
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.platform import resource_loader

_gru_ops_so = loader.load_op_library(
    resource_loader.get_path_to_datafile("_gru_ops.so"))


@ops.RegisterGradient("GRUBlockCell")
def _GRUBlockCellGrad(op, *grad):
  r"""Gradient for GRUBlockCell.

  Args:
    op: Op for which the gradient is defined.
    *grad: Gradients of the optimization function wrt output
      for the Op.

  Returns:
    d_x: Gradients wrt to x
    d_h: Gradients wrt to h
    d_w_ru: Gradients wrt to w_ru
    d_w_c: Gradients wrt to w_c
    d_b_ru: Gradients wrt to b_ru
    d_b_c: Gradients wrt to b_c

  Mathematics behind the Gradients below:
  ```
  d_c_bar = d_h \circ (1-u) \circ (1-c \circ c)
  d_u_bar = d_h \circ (h-c) \circ u \circ (1-u)

  d_r_bar_u_bar = [d_r_bar d_u_bar]

  [d_x_component_1 d_h_prev_component_1] = d_r_bar_u_bar * w_ru^T

  [d_x_component_2 d_h_prevr] = d_c_bar * w_c^T

  d_x = d_x_component_1 + d_x_component_2

  d_h_prev = d_h_prev_component_1 + d_h_prevr \circ r + u
  ```
  Below calculation is performed in the python wrapper for the Gradients
  (not in the gradient kernel.)
  ```
  d_w_ru = x_h_prevr^T * d_c_bar

  d_w_c = x_h_prev^T * d_r_bar_u_bar

  d_b_ru = sum of d_r_bar_u_bar along axis = 0

  d_b_c = sum of d_c_bar along axis = 0
  ```
  """
  x, h_prev, w_ru, w_c, b_ru, b_c = op.inputs
  r, u, c, _ = op.outputs
  _, _, _, d_h = grad

  d_x, d_h_prev, d_c_bar, d_r_bar_u_bar = _gru_ops_so.gru_block_cell_grad(
      x, h_prev, w_ru, w_c, b_ru, b_c, r, u, c, d_h)

  x_h_prev = array_ops.concat(1, [x, h_prev])
  d_w_ru = math_ops.matmul(x_h_prev, d_r_bar_u_bar, transpose_a=True)
  d_b_ru = nn_ops.bias_add_grad(d_r_bar_u_bar)

  x_h_prevr = array_ops.concat(1, [x, h_prev * r])
  d_w_c = math_ops.matmul(x_h_prevr, d_c_bar, transpose_a=True)
  d_b_c = nn_ops.bias_add_grad(d_c_bar)

  return d_x, d_h_prev, d_w_ru, d_w_c, d_b_ru, d_b_c


class GRUBlockCell(rnn_cell.RNNCell):
  r"""Block GRU cell implementation.

  The implementation is based on:  http://arxiv.org/abs/1406.1078
  Computes the LSTM cell forward propagation for 1 time step.

  This kernel op implements the following mathematical equations:

  Biases are initialized with:

  * `b_ru` - constant_initializer(1.0)
  * `b_c` - constant_initializer(0.0)

  ```
  x_h_prev = [x, h_prev]

  [r_bar u_bar] = x_h_prev * w_ru + b_ru

  r = sigmoid(r_bar)
  u = sigmoid(u_bar)

  h_prevr = h_prev \circ r

  x_h_prevr = [x h_prevr]

  c_bar = x_h_prevr * w_c + b_c
  c = tanh(c_bar)

  h = (1-u) \circ c + u \circ h_prev
  ```

  """

  def __init__(self, cell_size):
    """Initialize the Block GRU cell.

    Args:
      cell_size: int, GRU cell size.
    """
    self._cell_size = cell_size

  @property
  def state_size(self):
    return self._cell_size

  @property
  def output_size(self):
    return self._cell_size

  def __call__(self, x, h_prev, scope=None):
    """GRU cell."""
    with vs.variable_scope(scope or type(self).__name__):
      input_size = x.get_shape().with_rank(2)[1]

      # Check if the input size exist.
      if input_size is None:
        raise ValueError("Expecting input_size to be set.")

      # Check cell_size == state_size from h_prev.
      cell_size = h_prev.get_shape().with_rank(2)[1]
      if cell_size != self._cell_size:
        raise ValueError("Shape of h_prev[1] incorrect: cell_size %i vs %s" %
                         (self._cell_size, cell_size))

      if cell_size is None:
        raise ValueError("cell_size from `h_prev` should not be None.")

      w_ru = vs.get_variable("w_ru", [input_size + self._cell_size,
                                      self._cell_size * 2])
      b_ru = vs.get_variable(
          "b_ru", [self._cell_size * 2],
          initializer=init_ops.constant_initializer(1.0))
      w_c = vs.get_variable("w_c",
                            [input_size + self._cell_size, self._cell_size])
      b_c = vs.get_variable(
          "b_c", [self._cell_size],
          initializer=init_ops.constant_initializer(0.0))

      _gru_block_cell = _gru_ops_so.gru_block_cell  # pylint: disable=invalid-name
      _, _, _, new_h = _gru_block_cell(
          x=x, h_prev=h_prev, w_ru=w_ru, w_c=w_c, b_ru=b_ru, b_c=b_c)

      return new_h, new_h
