# 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.
# ==============================================================================

"""Simple bidirectional model definitions."""

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

import tensorflow as tf
from regularization import variational_dropout

FLAGS = tf.app.flags.FLAGS


def discriminator(hparams,
                  sequence,
                  is_training,
                  reuse=None,
                  initial_state=None):
  """Define the Discriminator graph."""
  sequence = tf.cast(sequence, tf.int32)

  if FLAGS.dis_share_embedding:
    assert hparams.dis_rnn_size == hparams.gen_rnn_size, (
        'If you wish to share Discriminator/Generator embeddings, they must be'
        ' same dimension.')
    with tf.variable_scope('gen/decoder/rnn', reuse=True):
      embedding = tf.get_variable('embedding',
                                  [FLAGS.vocab_size, hparams.gen_rnn_size])

  with tf.variable_scope('dis', reuse=reuse):

    def lstm_cell():
      return tf.contrib.rnn.BasicLSTMCell(
          hparams.dis_rnn_size,
          forget_bias=0.0,
          state_is_tuple=True,
          reuse=reuse)

    attn_cell = lstm_cell
    if is_training and hparams.dis_vd_keep_prob < 1:

      def attn_cell():
        return variational_dropout.VariationalDropoutWrapper(
            lstm_cell(), FLAGS.batch_size, hparams.dis_rnn_size,
            hparams.dis_vd_keep_prob, hparams.dis_vd_keep_prob)

    cell_fwd = tf.contrib.rnn.MultiRNNCell(
        [attn_cell() for _ in range(hparams.dis_num_layers)],
        state_is_tuple=True)

    cell_bwd = tf.contrib.rnn.MultiRNNCell(
        [attn_cell() for _ in range(hparams.dis_num_layers)],
        state_is_tuple=True)

    # print initial_state
    # print cell_fwd.zero_state(FLAGS.batch_size, tf.float32)
    if initial_state:
      state_fwd = [[tf.identity(x) for x in inner_initial_state]
                   for inner_initial_state in initial_state]
      state_bwd = cell_bwd.zero_state(FLAGS.batch_size, tf.float32)
    else:
      state_fwd = cell_fwd.zero_state(FLAGS.batch_size, tf.float32)
      state_bwd = cell_bwd.zero_state(FLAGS.batch_size, tf.float32)

    def make_mask(keep_prob, units):
      random_tensor = keep_prob
      # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
      random_tensor += tf.random_uniform(tf.stack([FLAGS.batch_size, units]))
      return tf.floor(random_tensor) / keep_prob

    if is_training:
      output_mask = make_mask(hparams.dis_vd_keep_prob,
                              2 * hparams.dis_rnn_size)

    if not FLAGS.dis_share_embedding:
      embedding = tf.get_variable('embedding',
                                  [FLAGS.vocab_size, hparams.dis_rnn_size])

    rnn_inputs = tf.nn.embedding_lookup(embedding, sequence)

    rnn_inputs = tf.unstack(rnn_inputs, axis=1)

    with tf.variable_scope('rnn') as vs:
      outputs, _, _ = tf.contrib.rnn.static_bidirectional_rnn(
          cell_fwd, cell_bwd, rnn_inputs, state_fwd, state_bwd, scope=vs)

      if is_training:
        outputs *= output_mask

      # Prediction is linear output for Discriminator.
      predictions = tf.contrib.layers.linear(outputs, 1, scope=vs)
      predictions = tf.transpose(predictions, [1, 0, 2])

  if FLAGS.baseline_method == 'critic':
    with tf.variable_scope('critic', reuse=reuse) as critic_scope:
      values = tf.contrib.layers.linear(outputs, 1, scope=critic_scope)
      values = tf.transpose(values, [1, 0, 2])

    return tf.squeeze(predictions, axis=2), tf.squeeze(values, axis=2)

  else:
    return tf.squeeze(predictions, axis=2), None
