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import os
from collections import defaultdict
from unittest.mock import patch
from parameterized import parameterized
from torchtext.datasets.penntreebank import PennTreebank
from ..common.case_utils import TempDirMixin, zip_equal, get_random_unicode
from ..common.torchtext_test_case import TorchtextTestCase
def _get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
base_dir = os.path.join(root_dir, "PennTreebank")
os.makedirs(base_dir, exist_ok=True)
seed = 1
mocked_data = defaultdict(list)
for file_name in ("ptb.train.txt", "ptb.valid.txt", "ptb.test.txt"):
txt_file = os.path.join(base_dir, file_name)
with open(txt_file, "w", encoding="utf-8") as f:
for i in range(5):
rand_string = get_random_unicode(seed)
dataset_line = f"{rand_string}"
# append line to correct dataset split
split = file_name.replace("ptb.", "").replace(".txt", "")
mocked_data[split].append(dataset_line)
f.write(f"{rand_string}\n")
seed += 1
return mocked_data
class TestPennTreebank(TempDirMixin, TorchtextTestCase):
root_dir = None
samples = []
@classmethod
def setUpClass(cls):
super().setUpClass()
cls.root_dir = cls.get_base_temp_dir()
cls.samples = _get_mock_dataset(os.path.join(cls.root_dir, "datasets"))
cls.patcher = patch("torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True)
cls.patcher.start()
@classmethod
def tearDownClass(cls):
cls.patcher.stop()
super().tearDownClass()
@parameterized.expand(["train", "valid", "test"])
def test_penn_treebank_polarity(self, split):
dataset = PennTreebank(root=self.root_dir, split=split)
samples = list(dataset)
expected_samples = self.samples[split]
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
@parameterized.expand(["train", "valid", "test"])
def test_penn_treebank_split_argument(self, split):
dataset1 = PennTreebank(root=self.root_dir, split=split)
(dataset2,) = PennTreebank(root=self.root_dir, split=(split,))
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
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