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Black formatting in fairseq/test_noising (#341)
Summary: Pull Request resolved: https://github.com/pytorch/fairseq/pull/341 Use black formatting in test_noising.py Reviewed By: xianxl Differential Revision: D12810285 fbshipit-source-id: 5517dd5d2f086831f487d88acf6bc2fa18820297
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@ -5,17 +5,17 @@
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# the root directory of this source tree. An additional grant of patent rights
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# can be found in the PATENTS file in the same directory.
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import torch
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import unittest
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import tests.utils as test_utils
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import torch
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from fairseq import utils
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from fairseq.data import (
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AppendEosDataset,
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Dictionary,
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LanguagePairDataset,
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data_utils,
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noising,
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LanguagePairDataset,
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)
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@ -69,12 +69,12 @@ class TestDataNoising(unittest.TestCase):
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"""Asserts last token of every sentence in x is EOS """
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for i in range(len(x_len)):
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self.assertEqual(
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x[x_len[i]-1][i],
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x[x_len[i] - 1][i],
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eos,
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(
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"Expected eos (token id {eos}) at the end of sentence {i} but "
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"got {other} instead"
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).format(i=i, eos=eos, other=x[i][-1])
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).format(i=i, eos=eos, other=x[i][-1]),
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)
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def assert_word_dropout_correct(self, x, x_noised, x_len, l_noised):
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@ -197,11 +197,11 @@ class TestDataNoising(unittest.TestCase):
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"""Asserts that the last token of each sentence in x is not EOS """
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for i in range(len(x_len)):
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self.assertNotEqual(
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x[x_len[i]-1][i],
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x[x_len[i] - 1][i],
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eos,
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"Expected no eos (token id {eos}) at the end of sentence {i}.".format(
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eos=eos, i=i,
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)
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eos=eos, i=i
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),
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)
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def test_word_dropout_without_eos(self):
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@ -273,10 +273,7 @@ class TestDataNoising(unittest.TestCase):
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if use_append_eos_dataset:
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tgt = AppendEosDataset(src_dataset, src_dict.eos())
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language_pair_dataset = LanguagePairDataset(
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src=noising_dataset,
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tgt=tgt,
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src_sizes=None,
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src_dict=src_dict
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src=noising_dataset, tgt=tgt, src_sizes=None, src_dict=src_dict
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)
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dataloader = torch.utils.data.DataLoader(
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@ -343,7 +340,7 @@ class TestDataNoising(unittest.TestCase):
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# Generated noisy source as source
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expected_src = torch.LongTensor(
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[[4, 5, 10, 11, 8, 12, 13], [pad, pad, pad, 6, 8, 9, 7]]
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[[4, 5, 10, 11, 8, 12, 13], [pad, pad, pad, 6, 8, 9, 7]]
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)
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# Original clean source as target (right-padded)
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expected_tgt = torch.LongTensor(
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@ -361,5 +358,5 @@ class TestDataNoising(unittest.TestCase):
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self.assertEqual(t1.ne(t2).long().sum(), 0)
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if __name__ == '__main__':
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if __name__ == "__main__":
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unittest.main()
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