remove duplicated code

requirements.txt

-absl-py==0.13.0
-biopython==1.79
-chex==0.0.7
-dm-haiku==0.0.4
-dm-tree==0.1.6
-immutabledict==2.0.0
-jax==0.3.14
-ml-collections==0.1.0
-numpy==1.19.5
-scipy==1.7.3
-tensorflow==2.7.0
-pandas==1.3.4
-protobuf==3.20.1

run_alphafold_test.py

-# 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 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, None)
-
-    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.append('relaxed_model1.pdb')
-    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()

setup.py

-# 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.
-"""Install script for setuptools."""
-
-from setuptools import find_packages
-from setuptools import setup
-
-setup(
-    name='alphafold',
-    version='2.2.1',
-    description='An implementation of the inference pipeline of AlphaFold v2.0.'
-    'This is a completely new model that was entered as AlphaFold2 in CASP14 '
-    'and published in Nature.',
-    author='DeepMind',
-    author_email='alphafold@deepmind.com',
-    license='Apache License, Version 2.0',
-    url='https://github.com/deepmind/alphafold',
-    packages=find_packages(),
-    install_requires=[
-        'absl-py',
-        'biopython',
-        'chex',
-        'dm-haiku',
-        'dm-tree',
-        'docker',
-        'immutabledict',
-        'jax',
-        'ml-collections',
-        'numpy',
-        'pandas',
-        'scipy',
-        'tensorflow-cpu',
-    ],
-    tests_require=['mock'],
-    classifiers=[
-        'Development Status :: 5 - Production/Stable',
-        'Intended Audience :: Science/Research',
-        'License :: OSI Approved :: Apache Software License',
-        'Operating System :: POSIX :: Linux',
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
-        'Programming Language :: Python :: 3.8',
-        'Programming Language :: Python :: 3.9',
-        'Programming Language :: Python :: 3.10',
-        'Topic :: Scientific/Engineering :: Artificial Intelligence',
-    ],
-)