<reponame>GingerBear/texar
#
"""
Unit tests for embedders.
"""

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

# pylint: disable=no-member

import numpy as np

import tensorflow as tf

from texar.tf.modules.embedders.embedders import WordEmbedder
from texar.tf.modules.embedders.position_embedders import PositionEmbedder
from texar.tf.context import global_mode

class EmbedderTest(tf.test.TestCase):
    """Tests parameterized embedder.
    """

    def _test_word_embedder(self, hparams):
        """Tests :class:`texar.tf.modules.WordEmbedder`.
        """
        embedder = WordEmbedder(
            vocab_size=100, hparams=hparams)

        inputs = tf.ones([64, 16], dtype=tf.int32)
        outputs = embedder(inputs)

        inputs_soft = tf.ones([64, 16, embedder.vocab_size], dtype=tf.float32)
        outputs_soft = embedder(soft_ids=inputs_soft)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]

        hparams_dim = hparams["dim"]
        if not isinstance(hparams["dim"], (list, tuple)):
            hparams_dim = [hparams["dim"]]

        self.assertEqual(outputs.shape, [64, 16] + emb_dim)
        self.assertEqual(outputs_soft.shape, [64, 16] + emb_dim)
        self.assertEqual(emb_dim, hparams_dim)
        self.assertEqual(embedder.vocab_size, 100)
        self.assertEqual(len(embedder.trainable_variables), 1)

        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            outputs_, outputs_soft_ = sess.run(
                [outputs, outputs_soft],
                feed_dict={global_mode(): tf.estimator.ModeKeys.TRAIN})
            self.assertEqual(outputs_.shape, (64, 16) + tuple(emb_dim))
            self.assertEqual(outputs_soft_.shape, (64, 16) + tuple(emb_dim))

        # Tests unknown input shapes
        inputs = tf.placeholder(dtype=tf.int64, shape=[None, None])
        outputs = embedder(inputs)
        self.assertEqual(len(outputs.get_shape()), 2 + len(hparams_dim))

        inputs_soft = tf.placeholder(dtype=tf.int64, shape=[None, None, None])
        outputs_soft = embedder(soft_ids=inputs_soft)
        self.assertEqual(len(outputs_soft.get_shape()), 2 + len(hparams_dim))


    def _test_position_embedder(self, hparams):
        """Tests :class:`texar.tf.modules.PositionEmbedder`.
        """
        pos_size = 100
        embedder = PositionEmbedder(
            position_size=pos_size, hparams=hparams)
        inputs = tf.ones([64, 16], dtype=tf.int32)
        outputs = embedder(inputs)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]

        hparams_dim = hparams["dim"]
        if not isinstance(hparams["dim"], (list, tuple)):
            hparams_dim = [hparams["dim"]]

        self.assertEqual(outputs.shape, [64, 16] + emb_dim)
        self.assertEqual(emb_dim, hparams_dim)
        self.assertEqual(embedder.position_size, 100)
        self.assertEqual(len(embedder.trainable_variables), 1)

        seq_length = tf.random_uniform([64], maxval=pos_size, dtype=tf.int32)
        outputs = embedder(sequence_length=seq_length)
        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            outputs_, max_seq_length = sess.run(
                [outputs, tf.reduce_max(seq_length)],
                feed_dict={global_mode(): tf.estimator.ModeKeys.TRAIN})
            self.assertEqual(outputs_.shape,
                             (64, max_seq_length) + tuple(emb_dim))

    def test_embedder(self):
        """Tests various embedders.
        """
        # no dropout
        hparams = {"dim": 1024, "dropout_rate": 0}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        # dropout with default strategy
        hparams = {"dim": 1024, "dropout_rate": 0.3}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0.3}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0.3}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        # dropout with different strategies
        hparams = {"dim": 1024, "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": 1024, "dropout_rate": 0.3,
                   "dropout_strategy": "item_type"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0.3,
                   "dropout_strategy": "item_type"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0.3,
                   "dropout_strategy": "item_type"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

    def test_embedder_multi_calls(self):
        """Tests embedders called by multiple times.
        """
        hparams = {"dim": 1024, "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        embedder = WordEmbedder(
            vocab_size=100, hparams=hparams)
        inputs = tf.ones([64, 16], dtype=tf.int32)
        outputs = embedder(inputs)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]
        self.assertEqual(outputs.shape, [64, 16] + emb_dim)

        # Call with inputs in a different shape
        inputs = tf.ones([64, 10, 20], dtype=tf.int32)
        outputs = embedder(inputs)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]
        self.assertEqual(outputs.shape, [64, 10, 20] + emb_dim)

    def test_word_embedder_soft_ids(self):
        """Tests the correctness of using soft ids.
        """
        init_value = np.expand_dims(np.arange(5), 1)
        embedder = WordEmbedder(init_value=init_value)

        ids = np.array([3])
        soft_ids = np.array([[0, 0, 0, 1, 0]])

        outputs = embedder(ids=ids)
        soft_outputs = embedder(soft_ids=soft_ids)

        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            outputs_, soft_outputs_ = sess.run([outputs, soft_outputs])
            self.assertEqual(outputs_, soft_outputs_)

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

#
"""
Unit tests for embedders.
"""

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

# pylint: disable=no-member

import numpy as np

import tensorflow as tf

from texar.tf.modules.embedders.embedders import WordEmbedder
from texar.tf.modules.embedders.position_embedders import PositionEmbedder
from texar.tf.context import global_mode

class EmbedderTest(tf.test.TestCase):
    """Tests parameterized embedder.
    """

    def _test_word_embedder(self, hparams):
        """Tests :class:`texar.tf.modules.WordEmbedder`.
        """
        embedder = WordEmbedder(
            vocab_size=100, hparams=hparams)

        inputs = tf.ones([64, 16], dtype=tf.int32)
        outputs = embedder(inputs)

        inputs_soft = tf.ones([64, 16, embedder.vocab_size], dtype=tf.float32)
        outputs_soft = embedder(soft_ids=inputs_soft)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]

        hparams_dim = hparams["dim"]
        if not isinstance(hparams["dim"], (list, tuple)):
            hparams_dim = [hparams["dim"]]

        self.assertEqual(outputs.shape, [64, 16] + emb_dim)
        self.assertEqual(outputs_soft.shape, [64, 16] + emb_dim)
        self.assertEqual(emb_dim, hparams_dim)
        self.assertEqual(embedder.vocab_size, 100)
        self.assertEqual(len(embedder.trainable_variables), 1)

        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            outputs_, outputs_soft_ = sess.run(
                [outputs, outputs_soft],
                feed_dict={global_mode(): tf.estimator.ModeKeys.TRAIN})
            self.assertEqual(outputs_.shape, (64, 16) + tuple(emb_dim))
            self.assertEqual(outputs_soft_.shape, (64, 16) + tuple(emb_dim))

        # Tests unknown input shapes
        inputs = tf.placeholder(dtype=tf.int64, shape=[None, None])
        outputs = embedder(inputs)
        self.assertEqual(len(outputs.get_shape()), 2 + len(hparams_dim))

        inputs_soft = tf.placeholder(dtype=tf.int64, shape=[None, None, None])
        outputs_soft = embedder(soft_ids=inputs_soft)
        self.assertEqual(len(outputs_soft.get_shape()), 2 + len(hparams_dim))


    def _test_position_embedder(self, hparams):
        """Tests :class:`texar.tf.modules.PositionEmbedder`.
        """
        pos_size = 100
        embedder = PositionEmbedder(
            position_size=pos_size, hparams=hparams)
        inputs = tf.ones([64, 16], dtype=tf.int32)
        outputs = embedder(inputs)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]

        hparams_dim = hparams["dim"]
        if not isinstance(hparams["dim"], (list, tuple)):
            hparams_dim = [hparams["dim"]]

        self.assertEqual(outputs.shape, [64, 16] + emb_dim)
        self.assertEqual(emb_dim, hparams_dim)
        self.assertEqual(embedder.position_size, 100)
        self.assertEqual(len(embedder.trainable_variables), 1)

        seq_length = tf.random_uniform([64], maxval=pos_size, dtype=tf.int32)
        outputs = embedder(sequence_length=seq_length)
        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            outputs_, max_seq_length = sess.run(
                [outputs, tf.reduce_max(seq_length)],
                feed_dict={global_mode(): tf.estimator.ModeKeys.TRAIN})
            self.assertEqual(outputs_.shape,
                             (64, max_seq_length) + tuple(emb_dim))

    def test_embedder(self):
        """Tests various embedders.
        """
        # no dropout
        hparams = {"dim": 1024, "dropout_rate": 0}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        # dropout with default strategy
        hparams = {"dim": 1024, "dropout_rate": 0.3}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0.3}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0.3}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        # dropout with different strategies
        hparams = {"dim": 1024, "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": 1024, "dropout_rate": 0.3,
                   "dropout_strategy": "item_type"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024], "dropout_rate": 0.3,
                   "dropout_strategy": "item_type"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

        hparams = {"dim": [1024, 10], "dropout_rate": 0.3,
                   "dropout_strategy": "item_type"}
        self._test_word_embedder(hparams)
        self._test_position_embedder(hparams)

    def test_embedder_multi_calls(self):
        """Tests embedders called by multiple times.
        """
        hparams = {"dim": 1024, "dropout_rate": 0.3,
                   "dropout_strategy": "item"}
        embedder = WordEmbedder(
            vocab_size=100, hparams=hparams)
        inputs = tf.ones([64, 16], dtype=tf.int32)
        outputs = embedder(inputs)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]
        self.assertEqual(outputs.shape, [64, 16] + emb_dim)

        # Call with inputs in a different shape
        inputs = tf.ones([64, 10, 20], dtype=tf.int32)
        outputs = embedder(inputs)

        emb_dim = embedder.dim
        if not isinstance(emb_dim, (list, tuple)):
            emb_dim = [emb_dim]
        self.assertEqual(outputs.shape, [64, 10, 20] + emb_dim)

    def test_word_embedder_soft_ids(self):
        """Tests the correctness of using soft ids.
        """
        init_value = np.expand_dims(np.arange(5), 1)
        embedder = WordEmbedder(init_value=init_value)

        ids = np.array([3])
        soft_ids = np.array([[0, 0, 0, 1, 0]])

        outputs = embedder(ids=ids)
        soft_outputs = embedder(soft_ids=soft_ids)

        with self.test_session() as sess:
            sess.run(tf.global_variables_initializer())
            outputs_, soft_outputs_ = sess.run([outputs, soft_outputs])
            self.assertEqual(outputs_, soft_outputs_)

if __name__ == "__main__":
    tf.test.main()
# Unit tests for embedders. # pylint: disable=no-member Tests parameterized embedder. Tests :class:`texar.tf.modules.WordEmbedder`. # Tests unknown input shapes Tests :class:`texar.tf.modules.PositionEmbedder`. Tests various embedders. # no dropout # dropout with default strategy # dropout with different strategies Tests embedders called by multiple times. # Call with inputs in a different shape Tests the correctness of using soft ids.