# Copyright 2017 Google Inc. 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.
# ==============================================================================
"""Pretrains a recurrent language model.

Computational time:
  2 days to train 100000 steps on 1 layer 1024 hidden units LSTM,
  256 embeddings, 400 truncated BP, 256 minibatch and on single GPU (Pascal
  Titan X, cuDNNv5).
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# Dependency imports

import tensorflow as tf

import graphs
import train_utils

FLAGS = tf.app.flags.FLAGS


def main(_):
  """Trains Language Model."""
  tf.logging.set_verbosity(tf.logging.INFO)
  with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
    model = graphs.get_model()
    train_op, loss, global_step = model.language_model_training()
    train_utils.run_training(train_op, loss, global_step)


if __name__ == '__main__':
  tf.app.run()