# Copyright 2021 DeepMind Technologies Limited
#
# 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 run_alphafold."""

import json
import os

from absl.testing import absltest
from absl.testing import parameterized
import run_alphafold
import mock
import numpy as np
# Internal import (7716).


class RunAlphafoldTest(parameterized.TestCase):

  @parameterized.named_parameters(
      ('relax', True),
      ('no_relax', False),
  )
  def test_end_to_end(self, do_relax):

    data_pipeline_mock = mock.Mock()
    model_runner_mock = mock.Mock()
    amber_relaxer_mock = mock.Mock()

    data_pipeline_mock.process.return_value = {}
    model_runner_mock.process_features.return_value = {
        'aatype': np.zeros((12, 10), dtype=np.int32),
        'residue_index': np.tile(np.arange(10, dtype=np.int32)[None], (12, 1)),
    }
    model_runner_mock.predict.return_value = {
        'structure_module': {
            'final_atom_positions': np.zeros((10, 37, 3)),
            'final_atom_mask': np.ones((10, 37)),
        },
        'predicted_lddt': {
            'logits': np.ones((10, 50)),
        },
        'plddt': np.ones(10) * 42,
        'ranking_confidence': 90,
        'ptm': np.array(0.),
        'aligned_confidence_probs': np.zeros((10, 10, 50)),
        'predicted_aligned_error': np.zeros((10, 10)),
        'max_predicted_aligned_error': np.array(0.),
    }
    model_runner_mock.multimer_mode = False
    amber_relaxer_mock.process.return_value = ('RELAXED', None, [1., 0., 0.])

    out_dir = self.create_tempdir().full_path
    fasta_path = os.path.join(out_dir, 'target.fasta')
    with open(fasta_path, 'wt') as f:
      f.write('>A\nAAAAAAAAAAAAA')
    fasta_name = 'test'

    run_alphafold.predict_structure(
        fasta_path=fasta_path,
        fasta_name=fasta_name,
        output_dir_base=out_dir,
        data_pipeline=data_pipeline_mock,
        model_runners={'model1': model_runner_mock},
        amber_relaxer=amber_relaxer_mock if do_relax else None,
        benchmark=False,
        random_seed=0)

    base_output_files = os.listdir(out_dir)
    self.assertIn('target.fasta', base_output_files)
    self.assertIn('test', base_output_files)

    target_output_files = os.listdir(os.path.join(out_dir, 'test'))
    expected_files = [
        'features.pkl', 'msas', 'ranked_0.pdb', 'ranking_debug.json',
        'result_model1.pkl', 'timings.json', 'unrelaxed_model1.pdb',
    ]
    if do_relax:
      expected_files.extend(['relaxed_model1.pdb', 'relax_metrics.json'])
      with open(os.path.join(out_dir, 'test', 'relax_metrics.json')) as f:
        relax_metrics = json.loads(f.read())
      self.assertDictEqual({'model1': {'remaining_violations': [1.0, 0.0, 0.0],
                                       'remaining_violations_count': 1.0}},
                           relax_metrics)
    self.assertCountEqual(expected_files, target_output_files)

    # Check that pLDDT is set in the B-factor column.
    with open(os.path.join(out_dir, 'test', 'unrelaxed_model1.pdb')) as f:
      for line in f:
        if line.startswith('ATOM'):
          self.assertEqual(line[61:66], '42.00')


if __name__ == '__main__':
  absltest.main()
