# coding=utf-8
# Copyright 2020 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.


"""Tests for Transformer."""

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

import numpy as np

from tensor2tensor.data_generators import problem_hparams
from tensor2tensor.models.research import universal_transformer

import tensorflow.compat.v1 as tf

BATCH_SIZE = 3
INPUT_LENGTH = 5
TARGET_LENGTH = 7
VOCAB_SIZE = 10


class UniversalTransformerTest(tf.test.TestCase):

  def get_model(self,
                hparams, mode=tf.estimator.ModeKeys.TRAIN, has_input=True):
    hparams.hidden_size = 8
    hparams.filter_size = 32
    hparams.num_heads = 1
    hparams.layer_prepostprocess_dropout = 0.0
    hparams.mix_with_transformer = ""

    p_hparams = problem_hparams.test_problem_hparams(VOCAB_SIZE,
                                                     VOCAB_SIZE,
                                                     hparams)
    if not has_input:
      del p_hparams.modality["inputs"]
    hparams.problems = [p_hparams]

    inputs = np.random.randint(
        VOCAB_SIZE, size=(BATCH_SIZE, INPUT_LENGTH, 1, 1))
    targets = np.random.randint(
        VOCAB_SIZE, size=(BATCH_SIZE, TARGET_LENGTH, 1, 1))
    features = {
        "targets": tf.constant(targets, dtype=tf.int32, name="targets"),
        "target_space_id": tf.constant(1, dtype=tf.int32)
    }
    if has_input:
      features["inputs"] = tf.constant(inputs, dtype=tf.int32, name="inputs")

    return universal_transformer.UniversalTransformer(
        hparams, mode, p_hparams), features

  def testTransformer(self):
    model, features = self.get_model(
        universal_transformer.universal_transformer_base())
    logits, _ = model(features)
    with self.test_session() as session:
      session.run(tf.global_variables_initializer())
      res = session.run(logits)
    self.assertEqual(res.shape, (BATCH_SIZE, TARGET_LENGTH, 1, 1, VOCAB_SIZE))


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