text: #%%
  # 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
  # limitations under the License.
  # ==============================================================================
  import collections
  import math
  import os
  import random
  import zipfile
  
  import numpy as np
  import urllib
  import tensorflow as tf
  
  # Step 1: Download the data.
  url = 'http://mattmahoney.net/dc/'
  
  def maybe_download(filename, expected_bytes):
    """Download a file if not present, and make sure it's the right size."""
    if not os.path.exists(filename):
      filename, _ = urllib.request.urlretrieve(url + filename, filename)
    statinfo = os.stat(filename)
    if statinfo.st_size == expected_bytes:
      print('Found and verified', filename)
    else:
      print(statinfo.st_size)
      raise Exception(
          'Failed to verify ' + filename + '. Can you get to it with a browser?')
    return filename
  
  filename = maybe_download('text8.zip', 31344016)
  
  
  # Read the data into a list of strings.
  def read_data(filename):
    """Extract the first file enclosed in a zip file as a list of words"""
    with zipfile.ZipFile(filename) as f:
      data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data
  
  words = read_data(filename)
  print('Data size', len(words))
  
  # Step 2: Build the dictionary and replace rare words with UNK token.
  vocabulary_size = 50000
  
  def build_dataset(words):
    count = [['UNK', -1]]
    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
    dictionary = dict()
    for word, _ in count:
      dictionary[word] = len(dictionary)
    data = list()
    unk_count = 0
    for word in words:
      if word in dictionary:
        index = dictionary[word]
      else:
        index = 0  # dictionary['UNK']
        unk_count += 1
      data.append(index)
    count[0][1] = unk_count
    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    return data, count, dictionary, reverse_dictionary
  
  data, count, dictionary, reverse_dictionary = build_dataset(words)
  del words  # Hint to reduce memory.
  print('Most common words (+UNK)', count[:5])
  print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
  
  data_index = 0
  
  
  # Step 3: Function to generate a training batch for the skip-gram Model.
  def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window
    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    span = 2 * skip_window + 1 # [ skip_window target skip_window ]
    buffer = collections.deque(maxlen=span)
    for _ in range(span):
      buffer.append(data[data_index])
      data_index = (data_index + 1) % len(data)
    for i in range(batch_size // num_skips):
      target = skip_window  # target label at the center of the buffer
      targets_to_avoid = [ skip_window ]
      for j in range(num_skips):
        while target in targets_to_avoid:
          target = random.randint(0, span - 1)
        targets_to_avoid.append(target)
        batch[i * num_skips + j] = buffer[skip_window]
        labels[i * num_skips + j, 0] = buffer[target]
      buffer.append(data[data_index])
      data_index = (data_index + 1) % len(data)
    return batch, labels
  
  batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
  for i in range(8):
    print(batch[i], reverse_dictionary[batch[i]],
        '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
  
  # Step 4: Build and train a skip-gram Model.
  
  batch_size = 128
  embedding_size = 128  # Dimension of the embedding vector.
  skip_window = 1       # How many words to consider left and right.
  num_skips = 2         # How many times to reuse an input to generate a label.
  
  # We pick a random validation set to sample nearest neighbors. Here we limit the
  # validation samples to the words that have a low numeric ID, which by
  # construction are also the most frequent.
  valid_size = 16     # Random set of words to evaluate similarity on.
  valid_window = 100  # Only pick dev samples in the head of the distribution.
  valid_examples = np.random.choice(valid_window, valid_size, replace=False)
  num_sampled = 64    # Number of negative examples to sample.
  
  
  graph = tf.Graph()
  with graph.as_default():
  
    # Input data.
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
  
    # Ops and variables pinned to the CPU because of missing GPU implementation
    with tf.device('/cpu:0'):
      # Look up embeddings for inputs.
      embeddings = tf.Variable(
          tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
      embed = tf.nn.embedding_lookup(embeddings, train_inputs)
  
      # Construct the variables for the NCE loss
      nce_weights = tf.Variable(
          tf.truncated_normal([vocabulary_size, embedding_size],
                              stddev=1.0 / math.sqrt(embedding_size)))
      nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
  
    # Compute the average NCE loss for the batch.
    # tf.nce_loss automatically draws a new sample of the negative labels each
    # time we evaluate the loss.
    loss = tf.reduce_mean(
        tf.nn.nce_loss(weights=nce_weights,
                       biases=nce_biases,
                       labels=train_labels,
                       inputs=embed,
                       num_sampled=num_sampled,
                       num_classes=vocabulary_size))
  
    # Construct the SGD optimizer using a learning rate of 1.0.
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
  
    # Compute the cosine similarity between minibatch examples and all embeddings.
    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
    normalized_embeddings = embeddings / norm
    valid_embeddings = tf.nn.embedding_lookup(
        normalized_embeddings, valid_dataset)
    similarity = tf.matmul(
        valid_embeddings, normalized_embeddings, transpose_b=True)
  
    # Add variable initializer.
    init = tf.global_variables_initializer()
  
  # Step 5: Begin training.
  num_steps = 100001
  
  with tf.Session(graph=graph) as session:
    # We must initialize all variables before we use them.
    init.run()
    print("Initialized")
  
    average_loss = 0
    for step in range(num_steps):
      batch_inputs, batch_labels = generate_batch(
          batch_size, num_skips, skip_window)
      feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}
  
      # We perform one update step by evaluating the optimizer op (including it
      # in the list of returned values for session.run()
      _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
      average_loss += loss_val
  
      if step % 2000 == 0:
        if step > 0:
          average_loss /= 2000
        # The average loss is an estimate of the loss over the last 2000 batches.
        print("Average loss at step ", step, ": ", average_loss)
        average_loss = 0
  
      # Note that this is expensive (~20% slowdown if computed every 500 steps)
      if step % 10000 == 0:
        sim = similarity.eval()
        for i in range(valid_size):
          valid_word = reverse_dictionary[valid_examples[i]]
          top_k = 8 # number of nearest neighbors
          nearest = (-sim[i, :]).argsort()[1:top_k+1]
          log_str = "Nearest to %s:" % valid_word
          for k in range(top_k):
            close_word = reverse_dictionary[nearest[k]]
            log_str = "%s %s," % (log_str, close_word)
          print(log_str)
    final_embeddings = normalized_embeddings.eval()
  
  # Step 6: Visualize the embeddings.
  
  def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
    assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
    plt.figure(figsize=(18, 18))  #in inches
    for i, label in enumerate(labels):
      x, y = low_dim_embs[i,:]
      plt.scatter(x, y)
      plt.annotate(label,
                   xy=(x, y),
                   xytext=(5, 2),
                   textcoords='offset points',
                   ha='right',
                   va='bottom')
  
    plt.savefig(filename)
  
    #%%
  try:
    from sklearn.manifold import TSNE
    import matplotlib.pyplot as plt
  
    tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
    plot_only = 200
    low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only,:])
    labels = [reverse_dictionary[i] for i in range(plot_only)]
    plot_with_labels(low_dim_embs, labels)
  
  except ImportError:
    print("Please install sklearn, matplotlib, and scipy to visualize embeddings.")
  