[s2s testing] turn all to unittests, use auto-delete temp dirs (#7859)

examples/seq2seq/test_bash_script.py

@@ -3,7 +3,6 @@
 import argparse
 import os
 import sys
-import tempfile
 from pathlib import Path
 from unittest.mock import patch
 
@@ -16,7 +15,7 @@ from distillation import BartSummarizationDistiller, distill_main
 from finetune import SummarizationModule, main
 from test_seq2seq_examples import CUDA_AVAILABLE, MBART_TINY
 from transformers import BartForConditionalGeneration, MarianMTModel
-from transformers.testing_utils import slow
+from transformers.testing_utils import TestCasePlus, slow
 from utils import load_json
 
 
@@ -24,18 +23,18 @@ MODEL_NAME = MBART_TINY
 MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
 
-@slow
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-def test_model_download():
+class TestAll(TestCasePlus):
+    @slow
+    @pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
+    def test_model_download(self):
         """This warms up the cache so that we can time the next test without including download time, which varies between machines."""
         BartForConditionalGeneration.from_pretrained(MODEL_NAME)
         MarianMTModel.from_pretrained(MARIAN_MODEL)
 
-
-@timeout_decorator.timeout(120)
-@slow
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-def test_train_mbart_cc25_enro_script():
+    @timeout_decorator.timeout(120)
+    @slow
+    @pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
+    def test_train_mbart_cc25_enro_script(self):
         data_dir = "examples/seq2seq/test_data/wmt_en_ro"
         env_vars_to_replace = {
             "--fp16_opt_level=O1": "",
@@ -53,7 +52,7 @@ def test_train_mbart_cc25_enro_script():
         bash_script = bash_script.replace("\\\n", "").strip().replace('"$@"', "")
         for k, v in env_vars_to_replace.items():
             bash_script = bash_script.replace(k, str(v))
-    output_dir = tempfile.mkdtemp(prefix="output_mbart")
+        output_dir = self.get_auto_remove_tmp_dir()
 
         bash_script = bash_script.replace("--fp16 ", "")
         testargs = (
@@ -81,7 +80,9 @@ def test_train_mbart_cc25_enro_script():
         metrics = load_json(model.metrics_save_path)
         first_step_stats = metrics["val"][0]
         last_step_stats = metrics["val"][-1]
-    assert len(metrics["val"]) == (args.max_epochs / args.val_check_interval) + 1  # +1 accounts for val_sanity_check
+        assert (
+            len(metrics["val"]) == (args.max_epochs / args.val_check_interval) + 1
+        )  # +1 accounts for val_sanity_check
 
         assert last_step_stats["val_avg_gen_time"] >= 0.01
 
@@ -106,11 +107,10 @@ def test_train_mbart_cc25_enro_script():
             # assert len(metrics["val"]) ==  desired_n_evals
             assert len(metrics["test"]) == 1
 
-
-@timeout_decorator.timeout(600)
-@slow
-@pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
-def test_opus_mt_distill_script():
+    @timeout_decorator.timeout(600)
+    @slow
+    @pytest.mark.skipif(not CUDA_AVAILABLE, reason="too slow to run on CPU")
+    def test_opus_mt_distill_script(self):
         data_dir = "examples/seq2seq/test_data/wmt_en_ro"
         env_vars_to_replace = {
             "--fp16_opt_level=O1": "",
@@ -131,7 +131,7 @@ def test_opus_mt_distill_script():
 
         for k, v in env_vars_to_replace.items():
             bash_script = bash_script.replace(k, str(v))
-    output_dir = tempfile.mkdtemp(prefix="marian_output")
+        output_dir = self.get_auto_remove_tmp_dir()
         bash_script = bash_script.replace("--fp16", "")
         epochs = 6
         testargs = (

examples/seq2seq/test_datasets.py

 import os
-import tempfile
 from pathlib import Path
 
 import numpy as np
@@ -7,11 +6,12 @@ import pytest
 from torch.utils.data import DataLoader
 
 from pack_dataset import pack_data_dir
+from parameterized import parameterized
 from save_len_file import save_len_file
 from test_seq2seq_examples import ARTICLES, BART_TINY, MARIAN_TINY, MBART_TINY, SUMMARIES, T5_TINY, make_test_data_dir
 from transformers import AutoTokenizer
 from transformers.modeling_bart import shift_tokens_right
-from transformers.testing_utils import slow
+from transformers.testing_utils import TestCasePlus, slow
 from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeq2SeqDataset, Seq2SeqDataset
 
 
@@ -19,9 +19,8 @@ BERT_BASE_CASED = "bert-base-cased"
 PEGASUS_XSUM = "google/pegasus-xsum"
 
 
-@slow
-@pytest.mark.parametrize(
-    "tok_name",
+class TestAll(TestCasePlus):
+    @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
@@ -29,10 +28,11 @@ PEGASUS_XSUM = "google/pegasus-xsum"
             BART_TINY,
             PEGASUS_XSUM,
         ],
-)
-def test_seq2seq_dataset_truncation(tok_name):
+    )
+    @slow
+    def test_seq2seq_dataset_truncation(self, tok_name):
         tokenizer = AutoTokenizer.from_pretrained(tok_name)
-    tmp_dir = make_test_data_dir()
+        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
         max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
         max_src_len = 4
@@ -68,11 +68,10 @@ def test_seq2seq_dataset_truncation(tok_name):
 
             break  # No need to test every batch
 
-
-@pytest.mark.parametrize("tok", [BART_TINY, BERT_BASE_CASED])
-def test_legacy_dataset_truncation(tok):
+    @parameterized.expand([BART_TINY, BERT_BASE_CASED])
+    def test_legacy_dataset_truncation(self, tok):
         tokenizer = AutoTokenizer.from_pretrained(tok)
-    tmp_dir = make_test_data_dir()
+        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
         max_len_source = max(len(tokenizer.encode(a)) for a in ARTICLES)
         max_len_target = max(len(tokenizer.encode(a)) for a in SUMMARIES)
         trunc_target = 4
@@ -94,13 +93,12 @@ def test_legacy_dataset_truncation(tok):
             assert max_len_target > trunc_target  # Truncated
             break  # No need to test every batch
 
-
-def test_pack_dataset():
+    def test_pack_dataset(self):
         tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")
 
-    tmp_dir = Path(make_test_data_dir())
+        tmp_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
         orig_examples = tmp_dir.joinpath("train.source").open().readlines()
-    save_dir = Path(tempfile.mkdtemp(prefix="packed_"))
+        save_dir = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
         pack_data_dir(tokenizer, tmp_dir, 128, save_dir)
         orig_paths = {x.name for x in tmp_dir.iterdir()}
         new_paths = {x.name for x in save_dir.iterdir()}
@@ -112,12 +110,11 @@ def test_pack_dataset():
         assert len(packed_examples[0]) == sum(len(x) for x in orig_examples)
         assert orig_paths == new_paths
 
-
-@pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
-def test_dynamic_batch_size():
+    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq")
+    def test_dynamic_batch_size(self):
         if not FAIRSEQ_AVAILABLE:
             return
-    ds, max_tokens, tokenizer = _get_dataset(max_len=64)
+        ds, max_tokens, tokenizer = self._get_dataset(max_len=64)
         required_batch_size_multiple = 64
         batch_sampler = ds.make_dynamic_sampler(max_tokens, required_batch_size_multiple=required_batch_size_multiple)
         batch_sizes = [len(x) for x in batch_sampler]
@@ -138,9 +135,8 @@ def test_dynamic_batch_size():
         if failures:
             raise AssertionError(f"too many tokens in {len(failures)} batches")
 
-
-def test_sortish_sampler_reduces_padding():
-    ds, _, tokenizer = _get_dataset(max_len=512)
+    def test_sortish_sampler_reduces_padding(self):
+        ds, _, tokenizer = self._get_dataset(max_len=512)
         bs = 2
         sortish_sampler = ds.make_sortish_sampler(bs, shuffle=False)
 
@@ -156,8 +152,7 @@ def test_sortish_sampler_reduces_padding():
         assert sum(count_pad_tokens(sortish_dl)) < sum(count_pad_tokens(naive_dl))
         assert len(sortish_dl) == len(naive_dl)
 
-
-def _get_dataset(n_obs=1000, max_len=128):
+    def _get_dataset(self, n_obs=1000, max_len=128):
         if os.getenv("USE_REAL_DATA", False):
             data_dir = "examples/seq2seq/wmt_en_ro"
             max_tokens = max_len * 2 * 64
@@ -179,16 +174,13 @@ def _get_dataset(n_obs=1000, max_len=128):
         )
         return ds, max_tokens, tokenizer
 
-
-def test_distributed_sortish_sampler_splits_indices_between_procs():
-    ds, max_tokens, tokenizer = _get_dataset()
+    def test_distributed_sortish_sampler_splits_indices_between_procs(self):
+        ds, max_tokens, tokenizer = self._get_dataset()
         ids1 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=0, add_extra_examples=False))
         ids2 = set(DistributedSortishSampler(ds, 256, num_replicas=2, rank=1, add_extra_examples=False))
         assert ids1.intersection(ids2) == set()
 
-
-@pytest.mark.parametrize(
-    "tok_name",
+    @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
@@ -196,13 +188,13 @@ def test_distributed_sortish_sampler_splits_indices_between_procs():
             BART_TINY,
             PEGASUS_XSUM,
         ],
-)
-def test_dataset_kwargs(tok_name):
+    )
+    def test_dataset_kwargs(self, tok_name):
         tokenizer = AutoTokenizer.from_pretrained(tok_name)
         if tok_name == MBART_TINY:
             train_dataset = Seq2SeqDataset(
                 tokenizer,
-            data_dir=make_test_data_dir(),
+                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
                 type_path="train",
                 max_source_length=4,
                 max_target_length=8,
@@ -213,7 +205,11 @@ def test_dataset_kwargs(tok_name):
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             train_dataset = Seq2SeqDataset(
-            tokenizer, data_dir=make_test_data_dir(), type_path="train", max_source_length=4, max_target_length=8
+                tokenizer,
+                data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()),
+                type_path="train",
+                max_source_length=4,
+                max_target_length=8,
             )
             kwargs = train_dataset.dataset_kwargs
             assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs

examples/seq2seq/test_finetune_trainer.py

 import os
 import sys
-import tempfile
 from unittest.mock import patch
 
-from transformers.testing_utils import slow
+from transformers.testing_utils import TestCasePlus, slow
 from transformers.trainer_callback import TrainerState
 from transformers.trainer_utils import set_seed
 
@@ -15,18 +14,18 @@ set_seed(42)
 MARIAN_MODEL = "sshleifer/student_marian_en_ro_6_1"
 
 
-def test_finetune_trainer():
-    output_dir = run_trainer(1, "12", MBART_TINY, 1)
+class TestFinetuneTrainer(TestCasePlus):
+    def test_finetune_trainer(self):
+        output_dir = self.run_trainer(1, "12", MBART_TINY, 1)
         logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
         eval_metrics = [log for log in logs if "eval_loss" in log.keys()]
         first_step_stats = eval_metrics[0]
         assert "eval_bleu" in first_step_stats
 
-
-@slow
-def test_finetune_trainer_slow():
+    @slow
+    def test_finetune_trainer_slow(self):
         # There is a missing call to __init__process_group somewhere
-    output_dir = run_trainer(eval_steps=2, max_len="128", model_name=MARIAN_MODEL, num_train_epochs=3)
+        output_dir = self.run_trainer(eval_steps=2, max_len="128", model_name=MARIAN_MODEL, num_train_epochs=3)
 
         # Check metrics
         logs = TrainerState.load_from_json(os.path.join(output_dir, "trainer_state.json")).log_history
@@ -43,10 +42,9 @@ def test_finetune_trainer_slow():
         assert "test_generations.txt" in contents
         assert "test_results.json" in contents
 
-
-def run_trainer(eval_steps: int, max_len: str, model_name: str, num_train_epochs: int):
+    def run_trainer(self, eval_steps: int, max_len: str, model_name: str, num_train_epochs: int):
         data_dir = "examples/seq2seq/test_data/wmt_en_ro"
-    output_dir = tempfile.mkdtemp(prefix="test_output")
+        output_dir = self.get_auto_remove_tmp_dir()
         argv = f"""
             --model_name_or_path {model_name}
             --data_dir {data_dir}

examples/seq2seq/test_seq2seq_examples.py

@@ -3,7 +3,6 @@ import logging
 import os
 import sys
 import tempfile
-import unittest
 from pathlib import Path
 from unittest.mock import patch
 
@@ -15,11 +14,12 @@ import lightning_base
 from convert_pl_checkpoint_to_hf import convert_pl_to_hf
 from distillation import distill_main
 from finetune import SummarizationModule, main
+from parameterized import parameterized
 from run_eval import generate_summaries_or_translations, run_generate
 from run_eval_search import run_search
 from transformers import AutoConfig, AutoModelForSeq2SeqLM
 from transformers.hf_api import HfApi
-from transformers.testing_utils import CaptureStderr, CaptureStdout, require_multigpu, require_torch_and_cuda, slow
+from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_and_cuda, slow
 from utils import ROUGE_KEYS, label_smoothed_nll_loss, lmap, load_json
 
 
@@ -52,7 +52,7 @@ CHEAP_ARGS = {
     "student_decoder_layers": 1,
     "val_check_interval": 1.0,
     "output_dir": "",
-    "fp16": False,  # TODO: set this to CUDA_AVAILABLE if ci installs apex or start using native amp
+    "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
     "no_teacher": False,
     "fp16_opt_level": "O1",
     "gpus": 1 if CUDA_AVAILABLE else 0,
@@ -88,6 +88,7 @@ CHEAP_ARGS = {
     "student_encoder_layers": 1,
     "freeze_encoder": False,
     "auto_scale_batch_size": False,
+    "overwrite_output_dir": False,
 }
 
 
@@ -110,15 +111,14 @@ logger.addHandler(stream_handler)
 logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
 
 
-def make_test_data_dir(**kwargs):
-    tmp_dir = Path(tempfile.mkdtemp(**kwargs))
+def make_test_data_dir(tmp_dir):
     for split in ["train", "val", "test"]:
-        _dump_articles((tmp_dir / f"{split}.source"), ARTICLES)
-        _dump_articles((tmp_dir / f"{split}.target"), SUMMARIES)
+        _dump_articles(os.path.join(tmp_dir, f"{split}.source"), ARTICLES)
+        _dump_articles(os.path.join(tmp_dir, f"{split}.target"), SUMMARIES)
     return tmp_dir
 
 
-class TestSummarizationDistiller(unittest.TestCase):
+class TestSummarizationDistiller(TestCasePlus):
     @classmethod
     def setUpClass(cls):
         logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
@@ -143,17 +143,6 @@ class TestSummarizationDistiller(unittest.TestCase):
                 failures.append(m)
         assert not failures, f"The following models could not be loaded through AutoConfig: {failures}"
 
-    @require_multigpu
-    @unittest.skip("Broken at the moment")
-    def test_multigpu(self):
-        updates = dict(
-            no_teacher=True,
-            freeze_encoder=True,
-            gpus=2,
-            sortish_sampler=True,
-        )
-        self._test_distiller_cli(updates, check_contents=False)
-
     def test_distill_no_teacher(self):
         updates = dict(student_encoder_layers=2, student_decoder_layers=1, no_teacher=True)
         self._test_distiller_cli(updates)
@@ -173,12 +162,12 @@ class TestSummarizationDistiller(unittest.TestCase):
         self.assertEqual(1, len(ckpts))
         transformer_ckpts = list(Path(model.output_dir).glob("**/*.bin"))
         self.assertEqual(len(transformer_ckpts), 2)
-        examples = lmap(str.strip, model.hparams.data_dir.joinpath("test.source").open().readlines())
-        out_path = tempfile.mktemp()
+        examples = lmap(str.strip, Path(model.hparams.data_dir).joinpath("test.source").open().readlines())
+        out_path = tempfile.mktemp()  # XXX: not being cleaned up
         generate_summaries_or_translations(examples, out_path, str(model.output_dir / "best_tfmr"))
         self.assertTrue(Path(out_path).exists())
 
-        out_path_new = tempfile.mkdtemp()
+        out_path_new = self.get_auto_remove_tmp_dir()
         convert_pl_to_hf(ckpts[0], transformer_ckpts[0].parent, out_path_new)
         assert os.path.exists(os.path.join(out_path_new, "pytorch_model.bin"))
 
@@ -253,8 +242,8 @@ class TestSummarizationDistiller(unittest.TestCase):
         )
         default_updates.update(updates)
         args_d: dict = CHEAP_ARGS.copy()
-        tmp_dir = make_test_data_dir()
-        output_dir = tempfile.mkdtemp(prefix="output_")
+        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+        output_dir = self.get_auto_remove_tmp_dir()
 
         args_d.update(data_dir=tmp_dir, output_dir=output_dir, **default_updates)
         model = distill_main(argparse.Namespace(**args_d))
@@ -279,13 +268,15 @@ class TestSummarizationDistiller(unittest.TestCase):
         return model
 
 
-def run_eval_tester(model):
-    input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
+class TestTheRest(TestCasePlus):
+    def run_eval_tester(self, model):
+        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
         output_file_name = input_file_name.parent / "utest_output.txt"
         assert not output_file_name.exists()
         articles = [" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
         _dump_articles(input_file_name, articles)
-    score_path = str(Path(tempfile.mkdtemp()) / "scores.json")
+
+        score_path = str(Path(self.get_auto_remove_tmp_dir()) / "scores.json")
         task = "translation_en_to_de" if model == T5_TINY else "summarization"
         testargs = f"""
             run_eval_search.py
@@ -301,27 +292,24 @@ def run_eval_tester(model):
         with patch.object(sys, "argv", testargs):
             run_generate()
             assert Path(output_file_name).exists()
-        os.remove(Path(output_file_name))
-
-
-# test one model to quickly (no-@slow) catch simple problems and do an
-# extensive testing of functionality with multiple models as @slow separately
-def test_run_eval():
-    run_eval_tester(T5_TINY)
+            # os.remove(Path(output_file_name))
 
+    # test one model to quickly (no-@slow) catch simple problems and do an
+    # extensive testing of functionality with multiple models as @slow separately
+    def test_run_eval(self):
+        self.run_eval_tester(T5_TINY)
 
-# any extra models should go into the list here - can be slow
-@slow
-@pytest.mark.parametrize("model", [BART_TINY, MBART_TINY])
-def test_run_eval_slow(model):
-    run_eval_tester(model)
-
+    # any extra models should go into the list here - can be slow
+    @parameterized.expand([BART_TINY, MBART_TINY])
+    @slow
+    def test_run_eval_slow(self, model):
+        self.run_eval_tester(model)
 
-# testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
-@slow
-@pytest.mark.parametrize("model", [T5_TINY, MBART_TINY])
-def test_run_eval_search(model):
-    input_file_name = Path(tempfile.mkdtemp()) / "utest_input.source"
+    # testing with 2 models to validate: 1. translation (t5) 2. summarization (mbart)
+    @parameterized.expand([T5_TINY, MBART_TINY])
+    @slow
+    def test_run_eval_search(self, model):
+        input_file_name = Path(self.get_auto_remove_tmp_dir()) / "utest_input.source"
         output_file_name = input_file_name.parent / "utest_output.txt"
         assert not output_file_name.exists()
 
@@ -334,7 +322,7 @@ def test_run_eval_search(model):
             ],
         }
 
-    tmp_dir = Path(tempfile.mkdtemp())
+        tmp_dir = Path(self.get_auto_remove_tmp_dir())
         score_path = str(tmp_dir / "scores.json")
         reference_path = str(tmp_dir / "val.target")
         _dump_articles(input_file_name, text["en"])
@@ -367,18 +355,16 @@ def test_run_eval_search(model):
             assert Path(output_file_name).exists()
             os.remove(Path(output_file_name))
 
-
-@pytest.mark.parametrize(
-    "model",
+    @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY],
-)
-def test_finetune(model):
+    )
+    def test_finetune(self, model):
         args_d: dict = CHEAP_ARGS.copy()
         task = "translation" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else "summarization"
         args_d["label_smoothing"] = 0.1 if task == "translation" else 0
 
-    tmp_dir = make_test_data_dir()
-    output_dir = tempfile.mkdtemp(prefix="output_")
+        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
+        output_dir = self.get_auto_remove_tmp_dir()
         args_d.update(
             data_dir=tmp_dir,
             model_name_or_path=model,
@@ -423,12 +409,11 @@ def test_finetune(model):
         assert isinstance(example_batch, dict)
         assert len(example_batch) >= 4
 
-
-def test_finetune_extra_model_args():
+    def test_finetune_extra_model_args(self):
         args_d: dict = CHEAP_ARGS.copy()
 
         task = "summarization"
-    tmp_dir = make_test_data_dir()
+        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
 
         args_d.update(
             data_dir=tmp_dir,
@@ -445,7 +430,7 @@ def test_finetune_extra_model_args():
 
         # test models whose config includes the extra_model_args
         model = BART_TINY
-    output_dir = tempfile.mkdtemp(prefix="output_1_")
+        output_dir = self.get_auto_remove_tmp_dir()
         args_d1 = args_d.copy()
         args_d1.update(
             model_name_or_path=model,
@@ -461,7 +446,7 @@ def test_finetune_extra_model_args():
 
         # test models whose config doesn't include the extra_model_args
         model = T5_TINY
-    output_dir = tempfile.mkdtemp(prefix="output_2_")
+        output_dir = self.get_auto_remove_tmp_dir()
         args_d2 = args_d.copy()
         args_d2.update(
             model_name_or_path=model,
@@ -474,15 +459,14 @@ def test_finetune_extra_model_args():
             model = main(args)
         assert str(excinfo.value) == f"model config doesn't have a `{unsupported_param}` attribute"
 
-
-def test_finetune_lr_schedulers():
+    def test_finetune_lr_schedulers(self):
         args_d: dict = CHEAP_ARGS.copy()
 
         task = "summarization"
-    tmp_dir = make_test_data_dir()
+        tmp_dir = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
 
         model = BART_TINY
-    output_dir = tempfile.mkdtemp(prefix="output_1_")
+        output_dir = self.get_auto_remove_tmp_dir()
 
         args_d.update(
             data_dir=tmp_dir,
@@ -531,4 +515,6 @@ def test_finetune_lr_schedulers():
         args_d1["lr_scheduler"] = supported_param
         args = argparse.Namespace(**args_d1)
         model = main(args)
-    assert getattr(model.hparams, "lr_scheduler") == supported_param, f"lr_scheduler={supported_param} shouldn't fail"
+        assert (
+            getattr(model.hparams, "lr_scheduler") == supported_param
+        ), f"lr_scheduler={supported_param} shouldn't fail"