# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  # Copyright (C) 2017 Intellisist, Inc. (Author: Hainan Xu)
  #
  # 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.
  # ==============================================================================
  
  # this script trains a vanilla RNNLM with TensorFlow. 
  # to call the script, do
  # python steps/tfrnnlm/lstm_fast.py --data_path=$datadir \
  #        --save_path=$savepath --vocab_path=$rnn.wordlist [--hidden-size=$size]
  #
  # One example recipe is at egs/ami/s5/local/tfrnnlm/run_vanilla_rnnlm.sh
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import sys
  
  import inspect
  import time
  
  import numpy as np
  import tensorflow as tf
  
  import reader
  
  flags = tf.flags
  logging = tf.logging
  
  flags.DEFINE_integer("hidden_size", 200, "hidden dim of RNN")
  
  flags.DEFINE_string("data_path", None,
                      "Where the training/test data is stored.")
  flags.DEFINE_string("vocab_path", None,
                      "Where the wordlist file is stored.")
  flags.DEFINE_string("save_path", None,
                      "Model output directory.")
  flags.DEFINE_bool("use_fp16", False,
                    "Train using 16-bit floats instead of 32bit floats")
  
  FLAGS = flags.FLAGS
  
  class Config(object):
    """Small config."""
    init_scale = 0.1
    learning_rate = 1
    max_grad_norm = 5
    num_layers = 2
    num_steps = 20
    hidden_size = 200
    max_epoch = 4
    max_max_epoch = 13
    keep_prob = 1.0
    lr_decay = 0.8
    batch_size = 64
  
  def data_type():
    return tf.float16 if FLAGS.use_fp16 else tf.float32
  
  # this new "softmax" function we show can train a "self-normalized" RNNLM where
  # the sum of the output is automatically (close to) 1.0
  # which saves a lot of computation for lattice-rescoring
  def new_softmax(labels, logits):
    target = tf.reshape(labels, [-1])
    f_logits = tf.exp(logits)
    row_sums = tf.reduce_sum(f_logits, 1) # this is the negative part of the objf
  
    t2 = tf.expand_dims(target, 1)
    range = tf.expand_dims(tf.range(tf.shape(target)[0]), 1)
    ind = tf.concat([range, t2], 1)
    res = tf.gather_nd(logits, ind)
  
    return -res + row_sums - 1
  
  class RnnlmInput(object):
    """The input data."""
  
    def __init__(self, config, data, name=None):
      self.batch_size = batch_size = config.batch_size
      self.num_steps = num_steps = config.num_steps
      self.epoch_size = ((len(data) // batch_size) - 1) // num_steps
      self.input_data, self.targets = reader.rnnlm_producer(
          data, batch_size, num_steps, name=name)
  
  
  class RnnlmModel(object):
    """The RNNLM model."""
  
    def __init__(self, is_training, config, input_):
      self._input = input_
  
      batch_size = input_.batch_size
      num_steps = input_.num_steps
      size = config.hidden_size
      vocab_size = config.vocab_size
  
      def lstm_cell():
        # With the latest TensorFlow source code (as of Mar 27, 2017),
        # the BasicLSTMCell will need a reuse parameter which is unfortunately not
        # defined in TensorFlow 1.0. To maintain backwards compatibility, we add
        # an argument check here:
        if 'reuse' in inspect.getargspec(
            tf.contrib.rnn.BasicLSTMCell.__init__).args:
          return tf.contrib.rnn.BasicLSTMCell(
              size, forget_bias=0.0, state_is_tuple=True,
              reuse=tf.get_variable_scope().reuse)
        else:
          return tf.contrib.rnn.BasicLSTMCell(
              size, forget_bias=0.0, state_is_tuple=True)
      attn_cell = lstm_cell
      if is_training and config.keep_prob < 1:
        def attn_cell():
          return tf.contrib.rnn.DropoutWrapper(
              lstm_cell(), output_keep_prob=config.keep_prob)
      self.cell = tf.contrib.rnn.MultiRNNCell(
          [attn_cell() for _ in range(config.num_layers)], state_is_tuple=True)
  
      self._initial_state = self.cell.zero_state(batch_size, data_type())
      self._initial_state_single = self.cell.zero_state(1, data_type())
  
      self.initial = tf.reshape(tf.stack(axis=0, values=self._initial_state_single), [config.num_layers, 2, 1, size], name="test_initial_state")
  
      # first implement the less efficient version
      test_word_in = tf.placeholder(tf.int32, [1, 1], name="test_word_in")
  
      state_placeholder = tf.placeholder(tf.float32, [config.num_layers, 2, 1, size], name="test_state_in")
      # unpacking the input state context 
      l = tf.unstack(state_placeholder, axis=0)
      test_input_state = tuple(
                 [tf.contrib.rnn.LSTMStateTuple(l[idx][0],l[idx][1])
                   for idx in range(config.num_layers)]
      )
  
      with tf.device("/cpu:0"):
        self.embedding = tf.get_variable(
            "embedding", [vocab_size, size], dtype=data_type())
  
        inputs = tf.nn.embedding_lookup(self.embedding, input_.input_data)
        test_inputs = tf.nn.embedding_lookup(self.embedding, test_word_in)
  
      # test time
      with tf.variable_scope("RNN"):
        (test_cell_output, test_output_state) = self.cell(test_inputs[:, 0, :], test_input_state)
  
      test_state_out = tf.reshape(tf.stack(axis=0, values=test_output_state), [config.num_layers, 2, 1, size], name="test_state_out")
      test_cell_out = tf.reshape(test_cell_output, [1, size], name="test_cell_out")
      # above is the first part of the graph for test
      # test-word-in
      #               > ---- > test-state-out
      # test-state-in        > test-cell-out
  
  
      # below is the 2nd part of the graph for test
      # test-word-out
      #               > prob(word | test-word-out)
      # test-cell-in
  
      test_word_out = tf.placeholder(tf.int32, [1, 1], name="test_word_out")
      cellout_placeholder = tf.placeholder(tf.float32, [1, size], name="test_cell_in")
  
      softmax_w = tf.get_variable(
          "softmax_w", [size, vocab_size], dtype=data_type())
      softmax_b = tf.get_variable("softmax_b", [vocab_size], dtype=data_type())
      softmax_b = softmax_b - 9.0
  
      test_logits = tf.matmul(cellout_placeholder, tf.transpose(tf.nn.embedding_lookup(tf.transpose(softmax_w), test_word_out[0]))) + softmax_b[test_word_out[0,0]]
  
      p_word = test_logits[0, 0]
      test_out = tf.identity(p_word, name="test_out")
  
      if is_training and config.keep_prob < 1:
        inputs = tf.nn.dropout(inputs, config.keep_prob)
  
      # Simplified version of models/tutorials/rnn/rnn.py's rnn().
      # This builds an unrolled LSTM for tutorial purposes only.
      # In general, use the rnn() or state_saving_rnn() from rnn.py.
      #
      # The alternative version of the code below is:
      #
      # inputs = tf.unstack(inputs, num=num_steps, axis=1)
      # outputs, state = tf.contrib.rnn.static_rnn(
      #     cell, inputs, initial_state=self._initial_state)
      outputs = []
      state = self._initial_state
      with tf.variable_scope("RNN"):
        for time_step in range(num_steps):
          if time_step > -1: tf.get_variable_scope().reuse_variables()
          (cell_output, state) = self.cell(inputs[:, time_step, :], state)
          outputs.append(cell_output)
  
      output = tf.reshape(tf.stack(axis=1, values=outputs), [-1, size])
      logits = tf.matmul(output, softmax_w) + softmax_b
      loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example(
          [logits],
          [tf.reshape(input_.targets, [-1])],
          [tf.ones([batch_size * num_steps], dtype=data_type())],
          softmax_loss_function=new_softmax)
      self._cost = cost = tf.reduce_sum(loss) / batch_size
      self._final_state = state
  
      if not is_training:
        return
  
      self._lr = tf.Variable(0.0, trainable=False)
      tvars = tf.trainable_variables()
      grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars),
                                        config.max_grad_norm)
      optimizer = tf.train.GradientDescentOptimizer(self._lr)
      self._train_op = optimizer.apply_gradients(
          list(zip(grads, tvars)),
          global_step=tf.contrib.framework.get_or_create_global_step())
  
      self._new_lr = tf.placeholder(
          tf.float32, shape=[], name="new_learning_rate")
      self._lr_update = tf.assign(self._lr, self._new_lr)
  
    def assign_lr(self, session, lr_value):
      session.run(self._lr_update, feed_dict={self._new_lr: lr_value})
  
    @property
    def input(self):
      return self._input
  
    @property
    def initial_state(self):
      return self._initial_state
  
    @property
    def cost(self):
      return self._cost
  
    @property
    def final_state(self):
      return self._final_state
  
    @property
    def lr(self):
      return self._lr
  
    @property
    def train_op(self):
      return self._train_op
  
  def run_epoch(session, model, eval_op=None, verbose=False):
    """Runs the model on the given data."""
    start_time = time.time()
    costs = 0.0
    iters = 0
    state = session.run(model.initial_state)
  
    fetches = {
        "cost": model.cost,
        "final_state": model.final_state,
    }
    if eval_op is not None:
      fetches["eval_op"] = eval_op
  
    for step in range(model.input.epoch_size):
      feed_dict = {}
      for i, (c, h) in enumerate(model.initial_state):
        feed_dict[c] = state[i].c
        feed_dict[h] = state[i].h
  
      vals = session.run(fetches, feed_dict)
      cost = vals["cost"]
      state = vals["final_state"]
  
  
      costs += cost
      iters += model.input.num_steps
  
      if verbose and step % (model.input.epoch_size // 10) == 10:
        print("%.3f perplexity: %.3f speed: %.0f wps" %
              (step * 1.0 / model.input.epoch_size, np.exp(costs / iters),
               iters * model.input.batch_size / (time.time() - start_time)))
  
    return np.exp(costs / iters)
  
  
  def get_config():
    return Config()
  
  def main(_):
    if not FLAGS.data_path:
      raise ValueError("Must set --data_path to RNNLM data directory")
  
    raw_data = reader.rnnlm_raw_data(FLAGS.data_path, FLAGS.vocab_path)
    train_data, valid_data, _, word_map = raw_data
  
    config = get_config()
    config.hidden_size = FLAGS.hidden_size
    config.vocab_size = len(word_map)
    eval_config = get_config()
    eval_config.batch_size = 1
    eval_config.num_steps = 1
  
    with tf.Graph().as_default():
      initializer = tf.random_uniform_initializer(-config.init_scale,
                                                  config.init_scale)
  
      with tf.name_scope("Train"):
        train_input = RnnlmInput(config=config, data=train_data, name="TrainInput")
        with tf.variable_scope("Model", reuse=None, initializer=initializer):
          m = RnnlmModel(is_training=True, config=config, input_=train_input)
        tf.summary.scalar("Training Loss", m.cost)
        tf.summary.scalar("Learning Rate", m.lr)
  
      with tf.name_scope("Valid"):
        valid_input = RnnlmInput(config=config, data=valid_data, name="ValidInput")
        with tf.variable_scope("Model", reuse=True, initializer=initializer):
          mvalid = RnnlmModel(is_training=False, config=config, input_=valid_input)
        tf.summary.scalar("Validation Loss", mvalid.cost)
  
      sv = tf.train.Supervisor(logdir=FLAGS.save_path)
      with sv.managed_session() as session:
        for i in range(config.max_max_epoch):
          lr_decay = config.lr_decay ** max(i + 1 - config.max_epoch, 0.0)
          m.assign_lr(session, config.learning_rate * lr_decay)
  
          print("Epoch: %d Learning rate: %.3f" % (i + 1, session.run(m.lr)))
          train_perplexity = run_epoch(session, m, eval_op=m.train_op,
                                       verbose=True)
  
          print("Epoch: %d Train Perplexity: %.3f" % (i + 1, train_perplexity))
          valid_perplexity = run_epoch(session, mvalid)
          print("Epoch: %d Valid Perplexity: %.3f" % (i + 1, valid_perplexity))
  
        if FLAGS.save_path:
          print("Saving model to %s." % FLAGS.save_path)
          sv.saver.save(session, FLAGS.save_path)
  
  if __name__ == "__main__":
    tf.app.run()