# Copyright 2015 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.
# ==============================================================================

"""Tests for word2vec_optimized module."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import tensorflow as tf

import word2vec_optimized

flags = tf.app.flags

FLAGS = flags.FLAGS


class Word2VecTest(tf.test.TestCase):

  def setUp(self):
    FLAGS.train_data = os.path.join(self.get_temp_dir() + "test-text.txt")
    FLAGS.eval_data = os.path.join(self.get_temp_dir() + "eval-text.txt")
    FLAGS.save_path = self.get_temp_dir()
    with open(FLAGS.train_data, "w") as f:
      f.write(
          """alice was beginning to get very tired of sitting by her sister on
          the bank, and of having nothing to do: once or twice she had peeped
          into the book her sister was reading, but it had no pictures or
          conversations in it, 'and what is the use of a book,' thought alice
          'without pictures or conversations?' So she was considering in her own
          mind (as well as she could, for the hot day made her feel very sleepy
          and stupid), whether the pleasure of making a daisy-chain would be
          worth the trouble of getting up and picking the daisies, when suddenly
          a White rabbit with pink eyes ran close by her.\n""")
      with open(FLAGS.eval_data, "w") as f:
        f.write("alice she rabbit once\n")

  def testWord2VecOptimized(self):
    FLAGS.batch_size = 5
    FLAGS.num_neg_samples = 10
    FLAGS.epochs_to_train = 1
    FLAGS.min_count = 0
    word2vec_optimized.main([])


if __name__ == "__main__":
  tf.test.main()
