#  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
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"""Example of Estimator for DNN-based text classification with DBpedia data."""


import argparse
import sys

import numpy as np
import pandas
from sklearn import metrics
import tensorflow as tf
from tensorflow.contrib.layers.python.layers import encoders

learn = tf.contrib.learn

FLAGS = None

MAX_DOCUMENT_LENGTH = 10
EMBEDDING_SIZE = 50
n_words = 0


def bag_of_words_model(features, target):
  """A bag-of-words model. Note it disregards the word order in the text."""
  target = tf.one_hot(target, 15, 1, 0)
  features = encoders.bow_encoder(
      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE)
  logits = tf.contrib.layers.fully_connected(features, 15, activation_fn=None)
  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)
  train_op = tf.contrib.layers.optimize_loss(
      loss,
      tf.contrib.framework.get_global_step(),
      optimizer='Adam',
      learning_rate=0.01)
  return ({
      'class': tf.argmax(logits, 1),
      'prob': tf.nn.softmax(logits)
  }, loss, train_op)


def rnn_model(features, target):
  """RNN model to predict from sequence of words to a class."""
  # Convert indexes of words into embeddings.
  # This creates embeddings matrix of [n_words, EMBEDDING_SIZE] and then
  # maps word indexes of the sequence into [batch_size, sequence_length,
  # EMBEDDING_SIZE].
  word_vectors = tf.contrib.layers.embed_sequence(
      features, vocab_size=n_words, embed_dim=EMBEDDING_SIZE, scope='words')

  # Split into list of embedding per word, while removing doc length dim.
  # word_list results to be a list of tensors [batch_size, EMBEDDING_SIZE].
  word_list = tf.unstack(word_vectors, axis=1)

  # Create a Gated Recurrent Unit cell with hidden size of EMBEDDING_SIZE.
  cell = tf.contrib.rnn.GRUCell(EMBEDDING_SIZE)

  # Create an unrolled Recurrent Neural Networks to length of
  # MAX_DOCUMENT_LENGTH and passes word_list as inputs for each unit.
  _, encoding = tf.contrib.rnn.static_rnn(cell, word_list, dtype=tf.float32)

  # Given encoding of RNN, take encoding of last step (e.g hidden size of the
  # neural network of last step) and pass it as features for logistic
  # regression over output classes.
  target = tf.one_hot(target, 15, 1, 0)
  logits = tf.contrib.layers.fully_connected(encoding, 15, activation_fn=None)
  loss = tf.contrib.losses.softmax_cross_entropy(logits, target)

  # Create a training op.
  train_op = tf.contrib.layers.optimize_loss(
      loss,
      tf.contrib.framework.get_global_step(),
      optimizer='Adam',
      learning_rate=0.01)

  return ({
      'class': tf.argmax(logits, 1),
      'prob': tf.nn.softmax(logits)
  }, loss, train_op)


def main(unused_argv):
  global n_words
  # Prepare training and testing data
  dbpedia = learn.datasets.load_dataset(
      'dbpedia', test_with_fake_data=FLAGS.test_with_fake_data)
  x_train = pandas.DataFrame(dbpedia.train.data)[1]
  y_train = pandas.Series(dbpedia.train.target)
  x_test = pandas.DataFrame(dbpedia.test.data)[1]
  y_test = pandas.Series(dbpedia.test.target)

  # Process vocabulary
  vocab_processor = learn.preprocessing.VocabularyProcessor(MAX_DOCUMENT_LENGTH)
  
  x_transform_train = vocab_processor.fit_transform(x_train)
  x_transform_test = vocab_processor.transform(x_test)
  
  x_train = np.array(list(x_transform_train))
  x_test = np.array(list(x_transform_test))
  
  n_words = len(vocab_processor.vocabulary_)
  print('Total words: %d' % n_words)

  # Build model
  # Switch between rnn_model and bag_of_words_model to test different models.
  model_fn = rnn_model
  if FLAGS.bow_model:
    model_fn = bag_of_words_model
  classifier = learn.Estimator(model_fn=model_fn)

  # Train and predict
  classifier.fit(x_train, y_train, steps=100)
  y_predicted = [
      p['class'] for p in classifier.predict(
          x_test, as_iterable=True)
  ]
  score = metrics.accuracy_score(y_test, y_predicted)
  print('Accuracy: {0:f}'.format(score))


if __name__ == '__main__':
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--test_with_fake_data',
      default=False,
      help='Test the example code with fake data.',
      action='store_true')
  parser.add_argument(
      '--bow_model',
      default=False,
      help='Run with BOW model instead of RNN.',
      action='store_true')
  FLAGS, unparsed = parser.parse_known_args()
  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
