# Copyright 2017 Google Inc.

#

# 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.

"""ByteNet tests."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

# Dependency imports

import numpy as np

from tensor2tensor.data_generators import problem_hparams

from tensor2tensor.models import bytenet

import tensorflow as tf

class ByteNetTest(tf.test.TestCase):

def testByteNet(self):

vocab_size = 9

x = np.random.random_integers(1, high=vocab_size - 1, size=(3, 5, 1, 1))

y = np.random.random_integers(1, high=vocab_size - 1, size=(3, 6, 1, 1))

hparams = bytenet.bytenet_base()

p_hparams = problem_hparams.test_problem_hparams(hparams, vocab_size,

vocab_size)

with self.test_session() as session:

features = {

"inputs": tf.constant(x, dtype=tf.int32),

"targets": tf.constant(y, dtype=tf.int32),

}

model = bytenet.ByteNet(hparams, p_hparams)

sharded_logits, _, _ = model.model_fn(features, True)

logits = tf.concat(sharded_logits, 0)

session.run(tf.global_variables_initializer())

res = session.run(logits)

self.assertEqual(res.shape, (3, 50, 1, 1, vocab_size))

if __name__ == "__main__":

tf.test.main()