# coding=utf-8

# Copyright 2017 The Tensor2Tensor Authors.

#

# 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.

"""tensor2tensor.problems test."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

# Dependency imports

from tensor2tensor import problems

import tensorflow as tf

MODES = tf.estimator.ModeKeys

class ProblemsTest(tf.test.TestCase):

def testBuildDataset(self):

# See all the available problems

self.assertTrue(len(problems.available()) > 10)

# Retrieve a problem by name

problem = problems.problem("translate_ende_wmt8k")

# Access train and dev datasets through Problem

train_dataset = problem.dataset(MODES.TRAIN)

dev_dataset = problem.dataset(MODES.EVAL)

# Access vocab size and other info (e.g. the data encoders used to

# encode/decode data for the feature, used below) through feature_info.

feature_info = problem.feature_info

self.assertTrue(feature_info["inputs"].vocab_size > 0)

self.assertTrue(feature_info["targets"].vocab_size > 0)

train_example = train_dataset.make_one_shot_iterator().get_next()

dev_example = dev_dataset.make_one_shot_iterator().get_next()

with tf.Session() as sess:

train_ex_val, _ = sess.run([train_example, dev_example])

_ = feature_info["inputs"].encoder.decode(train_ex_val["inputs"])

_ = feature_info["targets"].encoder.decode(train_ex_val["targets"])

if __name__ == "__main__":

tf.test.main()