# 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 Genetics problems."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np

from tensor2tensor.data_generators import dna_encoder
from tensor2tensor.data_generators import gene_expression

import tensorflow.compat.v1 as tf


class GeneticsTest(tf.test.TestCase):

  def _one_hot_bases(self, bases):
    ref = ["A", "C", "T", "G"]
    one_hots = []
    for base in bases:
      one_hot = [False] * 4
      if base in ref:
        one_hot[ref.index(base)] = True
      one_hots.append(one_hot)
    return np.array(one_hots)

  def testRecordToExample(self):
    encoder = dna_encoder.DNAEncoder(chunk_size=2)
    raw_inputs = ["A", "C", "G", "N", "C", "T"]

    # Put in numpy arrays in the same format as in the h5 file
    inputs = self._one_hot_bases(raw_inputs)
    mask = np.array([True, False, True])
    outputs = np.array([[1.0, 2.0, 3.0], [5.0, 1.0, 0.2], [5.1, 2.3, 2.3]])
    # Convert to example dict
    ex_dict = gene_expression.to_example_dict(encoder, inputs, mask, outputs)

    self.assertEqual(len(raw_inputs) // 2 + 1, len(ex_dict["inputs"]))
    self.assertAllEqual(encoder.encode(raw_inputs) + [1], ex_dict["inputs"])
    self.assertAllEqual([1.0, 0.0, 1.0], ex_dict["targets_mask"])
    self.assertAllEqual([1.0, 2.0, 3.0, 5.0, 1.0, 0.2, 5.1, 2.3, 2.3],
                        ex_dict["targets"])
    self.assertAllEqual([3, 3], ex_dict["targets_shape"])

  def testGenerateShardArgs(self):
    num_examples = 37
    num_shards = 4
    outfiles = [str(i) for i in range(num_shards)]
    shard_args = gene_expression.generate_shard_args(outfiles, num_examples)

    starts, ends, fnames = zip(*shard_args)
    self.assertAllEqual([0, 9, 18, 27], starts)
    self.assertAllEqual([9, 18, 27, 37], ends)
    self.assertAllEqual(fnames, outfiles)


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