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| # coding=utf-8 | |
| # Copyright 2019 HuggingFace Inc. | |
| # | |
| # 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. | |
| import unittest | |
| import numpy as np | |
| import torch | |
| from utils_summarization import build_mask, compute_token_type_ids, fit_to_block_size, process_story | |
| class SummarizationDataProcessingTest(unittest.TestCase): | |
| def setUp(self): | |
| self.block_size = 10 | |
| def test_fit_to_block_sequence_too_small(self): | |
| """ Pad the sequence with 0 if the sequence is smaller than the block size.""" | |
| sequence = [1, 2, 3, 4] | |
| expected_output = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0] | |
| self.assertEqual(fit_to_block_size(sequence, self.block_size, 0), expected_output) | |
| def test_fit_to_block_sequence_fit_exactly(self): | |
| """ Do nothing if the sequence is the right size. """ | |
| sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
| expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
| self.assertEqual(fit_to_block_size(sequence, self.block_size, 0), expected_output) | |
| def test_fit_to_block_sequence_too_big(self): | |
| """ Truncate the sequence if it is too long. """ | |
| sequence = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] | |
| expected_output = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] | |
| self.assertEqual(fit_to_block_size(sequence, self.block_size, 0), expected_output) | |
| def test_process_story_no_highlights(self): | |
| """ Processing a story with no highlights returns an empty list for the summary. | |
| """ | |
| raw_story = """It was the year of Our Lord one thousand seven hundred and | |
| seventy-five.\n\nSpiritual revelations were conceded to England at that | |
| favoured period, as at this.""" | |
| _, summary_lines = process_story(raw_story) | |
| self.assertEqual(summary_lines, []) | |
| def test_process_empty_story(self): | |
| """ An empty story returns an empty collection of lines. | |
| """ | |
| raw_story = "" | |
| story_lines, summary_lines = process_story(raw_story) | |
| self.assertEqual(story_lines, []) | |
| self.assertEqual(summary_lines, []) | |
| def test_process_story_with_missing_period(self): | |
| raw_story = ( | |
| "It was the year of Our Lord one thousand seven hundred and " | |
| "seventy-five\n\nSpiritual revelations were conceded to England " | |
| "at that favoured period, as at this.\n@highlight\n\nIt was the best of times" | |
| ) | |
| story_lines, summary_lines = process_story(raw_story) | |
| expected_story_lines = [ | |
| "It was the year of Our Lord one thousand seven hundred and seventy-five.", | |
| "Spiritual revelations were conceded to England at that favoured period, as at this.", | |
| ] | |
| self.assertEqual(expected_story_lines, story_lines) | |
| expected_summary_lines = ["It was the best of times."] | |
| self.assertEqual(expected_summary_lines, summary_lines) | |
| def test_build_mask_no_padding(self): | |
| sequence = torch.tensor([1, 2, 3, 4]) | |
| expected = torch.tensor([1, 1, 1, 1]) | |
| np.testing.assert_array_equal(build_mask(sequence, 0).numpy(), expected.numpy()) | |
| def test_build_mask(self): | |
| sequence = torch.tensor([1, 2, 3, 4, 23, 23, 23]) | |
| expected = torch.tensor([1, 1, 1, 1, 0, 0, 0]) | |
| np.testing.assert_array_equal(build_mask(sequence, 23).numpy(), expected.numpy()) | |
| def test_build_mask_with_padding_equal_to_one(self): | |
| sequence = torch.tensor([8, 2, 3, 4, 1, 1, 1]) | |
| expected = torch.tensor([1, 1, 1, 1, 0, 0, 0]) | |
| np.testing.assert_array_equal(build_mask(sequence, 1).numpy(), expected.numpy()) | |
| def test_compute_token_type_ids(self): | |
| separator = 101 | |
| batch = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]) | |
| expected = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]) | |
| result = compute_token_type_ids(batch, separator) | |
| np.testing.assert_array_equal(result, expected) | |