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  # to you 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
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  # under the License.
  
  """Gluon Text Sentiment Classification Example using RNN/CNN
  Example modified from below link:
  https://github.com/d2l-ai/d2l-en/blob/master/chapter_natural-language-processing/sentiment-analysis-rnn.md
  https://github.com/d2l-ai/d2l-en/blob/master/chapter_natural-language-processing/sentiment-analysis-cnn.md"""
  
  import collections
  import os
  import random
  import sys
  import tarfile
  
  import mxnet as mx
  from mxnet import nd, gluon
  from mxnet.contrib import text
  from mxnet.gluon import nn, rnn
  from mxnet.gluon.contrib.estimator import estimator
  
  # use with_seed decorator in python/unittest/common.py
  sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', 'python', 'unittest'))
  from common import with_seed
  import unittest
  
  
  class TextCNN(nn.Block):
      def __init__(self, vocab, embed_size, kernel_sizes, num_channels,
                   **kwargs):
          super(TextCNN, self).__init__(**kwargs)
          self.embedding = nn.Embedding(len(vocab), embed_size)
          # The embedding layer does not participate in training
          self.constant_embedding = nn.Embedding(len(vocab), embed_size)
          self.dropout = nn.Dropout(0.5)
          self.decoder = nn.Dense(2)
          # The max-over-time pooling layer has no weight, so it can share an
          # instance
          self.pool = nn.GlobalMaxPool1D()
          # Create multiple one-dimensional convolutional layers
          self.convs = nn.Sequential()
          for c, k in zip(num_channels, kernel_sizes):
              self.convs.add(nn.Conv1D(c, k, activation='relu'))
  
      def forward(self, inputs):
          # Concatenate the output of two embedding layers with shape of
          # (batch size, number of words, word vector dimension) by word vector
          embeddings = nd.concat(
              self.embedding(inputs), self.constant_embedding(inputs), dim=2)
          # According to the input format required by Conv1D, the word vector
          # dimension, that is, the channel dimension of the one-dimensional
          # convolutional layer, is transformed into the previous dimension
          embeddings = embeddings.transpose((0, 2, 1))
          # For each one-dimensional convolutional layer, after max-over-time
          # pooling, an NDArray with the shape of (batch size, channel size, 1)
          # can be obtained. Use the flatten function to remove the last
          # dimension and then concatenate on the channel dimension
          encoding = nd.concat(*[nd.flatten(
              self.pool(conv(embeddings))) for conv in self.convs], dim=1)
          # After applying the dropout method, use a fully connected layer to
          # obtain the output
          outputs = self.decoder(self.dropout(encoding))
          return outputs
  
  
  class BiRNN(nn.Block):
      def __init__(self, vocab, embed_size, num_hiddens, num_layers, **kwargs):
          super(BiRNN, self).__init__(**kwargs)
          self.embedding = nn.Embedding(len(vocab), embed_size)
          # Set Bidirectional to True to get a bidirectional recurrent neural
          # network
          self.encoder = rnn.LSTM(num_hiddens, num_layers=num_layers,
                                  bidirectional=True, input_size=embed_size)
          self.decoder = nn.Dense(2)
  
      def forward(self, inputs):
          # The shape of inputs is (batch size, number of words). Because LSTM
          # needs to use sequence as the first dimension, the input is
          # transformed and the word feature is then extracted. The output shape
          # is (number of words, batch size, word vector dimension).
          embeddings = self.embedding(inputs.T)
          # The shape of states is (number of words, batch size, 2 * number of
          # hidden units).
          states = self.encoder(embeddings)
          # Concatenate the hidden states of the initial time step and final
          # time step to use as the input of the fully connected layer. Its
          # shape is (batch size, 4 * number of hidden units)
          encoding = nd.concat(states[0], states[-1])
          outputs = self.decoder(encoding)
          return outputs
  
  
  def download_imdb(data_dir='/tmp/data'):
      '''
      Download and extract the IMDB dataset
      '''
      # Large Movie Review Dataset from http://ai.stanford.edu/~amaas/data/sentiment/
      # Note this dataset is copyright to Andrew Maas and Stanford AI Lab
      # @InProceedings{maas-EtAl:2011:ACL-HLT2011,
      #   author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},
      #   title     = {Learning Word Vectors for Sentiment Analysis},
      #   booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
      #   month     = {June},
      #   year      = {2011},
      #   address   = {Portland, Oregon, USA},
      #   publisher = {Association for Computational Linguistics},
      #   pages     = {142--150},
      #   url       = {http://www.aclweb.org/anthology/P11-1015}
      # }
      url = ('https://aws-ml-platform-datasets.s3.amazonaws.com/imdb/aclImdb_v1.tar.gz')
      sha1 = '01ada507287d82875905620988597833ad4e0903'
      if not os.path.exists(data_dir):
          os.makedirs(data_dir)
      file_path = os.path.join(data_dir, 'aclImdb_v1.tar.gz')
      if not os.path.isfile(file_path):
          file_path = gluon.utils.download(url, data_dir, sha1_hash=sha1)
      with tarfile.open(file_path, 'r') as f:
          f.extractall(data_dir)
  
  
  def read_imdb(folder='train'):
      '''
      Read the IMDB dataset
      '''
      data = []
      for label in ['pos', 'neg']:
          folder_name = os.path.join('/tmp/data/aclImdb/', folder, label)
          for file in os.listdir(folder_name):
              with open(os.path.join(folder_name, file), 'rb') as f:
                  review = f.read().decode('utf-8').replace('\n', '').lower()
                  data.append([review, 1 if label == 'pos' else 0])
      random.shuffle(data)
      return data
  
  
  def get_tokenized_imdb(data):
      '''
      Tokenized the words
      '''
  
      def tokenizer(text):
          return [tok.lower() for tok in text.split(' ')]
  
      return [tokenizer(review) for review, _ in data]
  
  
  def get_vocab_imdb(data):
      '''
      Get the indexed tokens
      '''
      tokenized_data = get_tokenized_imdb(data)
      counter = collections.Counter([tk for st in tokenized_data for tk in st])
      return text.vocab.Vocabulary(counter, min_freq=5)
  
  
  def preprocess_imdb(data, vocab):
      '''
      Make the length of each comment 500 by truncating or adding 0s
      '''
      max_l = 500
  
      def pad(x):
          return x[:max_l] if len(x) > max_l else x + [0] * (max_l - len(x))
  
      tokenized_data = get_tokenized_imdb(data)
      features = nd.array([pad(vocab.to_indices(x)) for x in tokenized_data])
      labels = nd.array([score for _, score in data])
      return features, labels
  
  
  def run(net, train_dataloader, test_dataloader, num_epochs, ctx, lr):
      '''
      Train a test sentiment model
      '''
  
      # Define trainer
      trainer = mx.gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': lr})
      # Define loss and evaluation metrics
      loss = gluon.loss.SoftmaxCrossEntropyLoss()
      acc = mx.metric.Accuracy()
  
      # Define estimator
      est = estimator.Estimator(net=net, loss=loss, metrics=acc,
                                trainer=trainer, context=ctx)
      # Begin training
      est.fit(train_data=train_dataloader, val_data=test_dataloader,
              epochs=num_epochs)
      return acc
  
  
  @with_seed()
  def test_estimator_cpu():
      '''
      Test estimator by doing one pass over each model with synthetic data
      '''
      models = ['TextCNN', 'BiRNN']
      ctx = mx.cpu()
      batch_size = 64
      embed_size = 100
      lr = 1
      num_epochs = 1
  
      train_data = mx.nd.random.randint(low=0, high=100, shape=(2 * batch_size, 500))
      train_label = mx.nd.random.randint(low=0, high=2, shape=(2 * batch_size,))
      val_data = mx.nd.random.randint(low=0, high=100, shape=(batch_size, 500))
      val_label = mx.nd.random.randint(low=0, high=2, shape=(batch_size,))
  
      train_dataloader = gluon.data.DataLoader(dataset=gluon.data.ArrayDataset(train_data, train_label),
                                               batch_size=batch_size, shuffle=True)
      val_dataloader = gluon.data.DataLoader(dataset=gluon.data.ArrayDataset(val_data, val_label),
                                             batch_size=batch_size)
      vocab_list = mx.nd.zeros(shape=(100,))
  
      # Get the model
      for model in models:
          if model == 'TextCNN':
              kernel_sizes, nums_channels = [3, 4, 5], [100, 100, 100]
              net = TextCNN(vocab_list, embed_size, kernel_sizes, nums_channels)
          else:
              num_hiddens, num_layers = 100, 2
              net = BiRNN(vocab_list, embed_size, num_hiddens, num_layers)
          net.initialize(mx.init.Xavier(), ctx=ctx)
  
          run(net, train_dataloader, val_dataloader, num_epochs=num_epochs, ctx=ctx, lr=lr)
  
  
  # using fixed seed to reduce flakiness in accuracy assertion
  @with_seed(7)
  @unittest.skipIf(mx.context.num_gpus() < 1, "skip if no GPU")
  def test_estimator_gpu():
      '''
      Test estimator by training Bidirectional RNN for 5 epochs on the IMDB dataset
      and verify accuracy
      '''
      ctx = mx.gpu(0)
      batch_size = 64
      num_epochs = 5
      embed_size = 100
      lr = 0.01
  
      # data
      download_imdb()
      train_data, test_data = read_imdb('train'), read_imdb('test')
      vocab = get_vocab_imdb(train_data)
  
      train_set = gluon.data.ArrayDataset(*preprocess_imdb(train_data, vocab))
      test_set = gluon.data.ArrayDataset(*preprocess_imdb(test_data, vocab))
      train_dataloader = gluon.data.DataLoader(train_set, batch_size, shuffle=True)
      test_dataloader = gluon.data.DataLoader(test_set, batch_size)
  
      # Model
      num_hiddens, num_layers = 100, 2
      net = BiRNN(vocab, embed_size, num_hiddens, num_layers)
      net.initialize(mx.init.Xavier(), ctx=ctx)
  
      glove_embedding = text.embedding.create(
          'glove', pretrained_file_name='glove.6B.100d.txt', vocabulary=vocab)
  
      net.embedding.weight.set_data(glove_embedding.idx_to_vec)
      net.embedding.collect_params().setattr('grad_req', 'null')
  
      acc = run(net, train_dataloader, test_dataloader, num_epochs=num_epochs, ctx=ctx, lr=lr)
  
      assert acc.get()[1] > 0.70
  
  
  if __name__ == '__main__':
      import nose
      nose.runmodule()
  