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import os
import zipfile
from collections import defaultdict
from unittest.mock import patch
from parameterized import parameterized
from torchtext.datasets.udpos import UDPOS
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, "UDPOS")
temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
os.makedirs(temp_dataset_dir, exist_ok=True)
seed = 1
mocked_data = defaultdict(list)
for file_name in ["train.txt", "dev.txt", "test.txt"]:
txt_file = os.path.join(temp_dataset_dir, file_name)
mocked_lines = mocked_data[os.path.splitext(file_name)[0]]
with open(txt_file, "w", encoding="utf-8") as f:
for i in range(5):
rand_strings = [get_random_unicode(seed)]
rand_label_1 = [get_random_unicode(seed)]
rand_label_2 = [get_random_unicode(seed)]
# one token per line (each sample ends with an extra \n)
for rand_string, label_1, label_2 in zip(rand_strings, rand_label_1, rand_label_2):
f.write(f"{rand_string}\t{label_1}\t{label_2}\n")
f.write("\n")
dataset_line = (rand_strings, rand_label_1, rand_label_2)
# append line to correct dataset split
mocked_lines.append(dataset_line)
seed += 1
# en-ud-v2.zip
compressed_dataset_path = os.path.join(base_dir, "en-ud-v2.zip")
# create zip file from dataset folder
with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
for file_name in ("train.txt", "dev.txt", "test.txt"):
txt_file = os.path.join(temp_dataset_dir, file_name)
zip_file.write(txt_file, arcname=os.path.join("UDPOS", file_name))
return mocked_data
class TestUDPOS(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_udpos(self, split):
dataset = UDPOS(root=self.root_dir, split=split)
samples = list(dataset)
expected_samples = self.samples[split] if split != "valid" else self.samples["dev"]
for sample, expected_sample in zip_equal(samples, expected_samples):
self.assertEqual(sample, expected_sample)
@parameterized.expand(["train", "valid", "test"])
def test_udpos_split_argument(self, split):
dataset1 = UDPOS(root=self.root_dir, split=split)
(dataset2,) = UDPOS(root=self.root_dir, split=(split,))
for d1, d2 in zip_equal(dataset1, dataset2):
self.assertEqual(d1, d2)
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