Mark tests with require_numpy1_on_windows

tests/features/test_features.py

@@ -25,7 +25,7 @@
 from datasets.info import DatasetInfo
 from datasets.utils.py_utils import asdict
 
-from ..utils import require_jax, require_tf, require_torch
+from ..utils import require_jax, require_numpy1_on_windows, require_tf, require_torch
 
 
 class FeaturesTest(TestCase):
@@ -543,6 +543,7 @@ def test_cast_to_python_objects_pandas_timedelta(self):
         casted_obj = cast_to_python_objects(pd.DataFrame({"a": [obj]}))
         self.assertDictEqual(casted_obj, {"a": [expected_obj]})
 
+    @require_numpy1_on_windows
     @require_torch
     def test_cast_to_python_objects_torch(self):
         import torch

tests/packaged_modules/test_webdataset.py

@@ -7,7 +7,7 @@
 from datasets import Audio, DownloadManager, Features, Image, Sequence, Value
 from datasets.packaged_modules.webdataset.webdataset import WebDataset
 
-from ..utils import require_librosa, require_pil, require_sndfile, require_torch
+from ..utils import require_librosa, require_numpy1_on_windows, require_pil, require_sndfile, require_torch
 
 
 @pytest.fixture
@@ -226,6 +226,7 @@ def test_webdataset_with_features(image_wds_file):
     assert isinstance(decoded["jpg"], PIL.Image.Image)
 
 
+@require_numpy1_on_windows
 @require_torch
 def test_tensor_webdataset(tensor_wds_file):
     import torch

tests/test_arrow_dataset.py

@@ -57,6 +57,7 @@
     require_dill_gt_0_3_2,
     require_jax,
     require_not_windows,
+    require_numpy1_on_windows,
     require_pil,
     require_polars,
     require_pyspark,
@@ -420,6 +421,7 @@ def test_set_format_numpy_multiple_columns(self, in_memory):
                 self.assertIsInstance(dset[0]["col_2"], np.str_)
                 self.assertEqual(dset[0]["col_2"].item(), "a")
 
+    @require_numpy_on_windows
     @require_torch
     def test_set_format_torch(self, in_memory):
         import torch
@@ -1525,6 +1527,7 @@ def func_return_multi_row_pd_dataframe(x):
             with self._create_dummy_dataset(in_memory, tmp_dir) as dset:
                 self.assertRaises(ValueError, dset.map, func_return_multi_row_pd_dataframe)
 
+    @require_numpy1_on_windows
     @require_torch
     def test_map_torch(self, in_memory):
         import torch
@@ -1590,6 +1593,7 @@ def func(example):
                     )
                     self.assertListEqual(dset_test[0]["tensor"], [1, 2, 3])
 
+    @require_numpy1_on_windows
     @require_torch
     def test_map_tensor_batched(self, in_memory):
         import torch

tests/test_dataset_dict.py

@@ -16,6 +16,7 @@
 from .utils import (
     assert_arrow_memory_doesnt_increase,
     assert_arrow_memory_increases,
+    require_numpy1_on_windows,
     require_polars,
     require_tf,
     require_torch,
@@ -109,6 +110,7 @@ def test_set_format_numpy(self):
             self.assertEqual(dset_split[0]["col_2"].item(), "a")
         del dset
 
+    @require_numpy1_on_windows
     @require_torch
     def test_set_format_torch(self):
         import torch

tests/test_fingerprint.py

@@ -21,6 +21,7 @@
 
 from .utils import (
     require_not_windows,
+    require_numpy1_on_windows,
     require_regex,
     require_spacy,
     require_tiktoken,
@@ -303,6 +304,7 @@ def test_hash_tiktoken_encoding(self):
         self.assertEqual(hash1, hash3)
         self.assertNotEqual(hash1, hash2)
 
+    @require_numpy1_on_windows
     @require_torch
     def test_hash_torch_tensor(self):
         import torch
@@ -316,6 +318,7 @@ def test_hash_torch_tensor(self):
         self.assertEqual(hash1, hash3)
         self.assertNotEqual(hash1, hash2)
 
+    @require_numpy1_on_windows
     @require_torch
     def test_hash_torch_generator(self):
         import torch

tests/test_formatting.py

@@ -21,6 +21,7 @@
 from .utils import (
     require_jax,
     require_librosa,
+    require_numpy1_on_windows,
     require_pil,
     require_polars,
     require_sndfile,
@@ -353,6 +354,7 @@ def test_polars_formatter(self):
         assert pl.Series.eq(batch["a"], pl.Series("a", _COL_A)).all()
         assert pl.Series.eq(batch["b"], pl.Series("b", _COL_B)).all()
 
+    @require_numpy1_on_windows
     @require_torch
     def test_torch_formatter(self):
         import torch
@@ -373,6 +375,7 @@ def test_torch_formatter(self):
         torch.testing.assert_close(batch["c"], torch.tensor(_COL_C, dtype=torch.float32))
         assert batch["c"].shape == np.array(_COL_C).shape
 
+    @require_numpy1_on_windows
     @require_torch
     def test_torch_formatter_torch_tensor_kwargs(self):
         import torch
@@ -389,6 +392,7 @@ def test_torch_formatter_torch_tensor_kwargs(self):
         self.assertEqual(batch["a"].dtype, torch.float16)
         self.assertEqual(batch["c"].dtype, torch.float16)
 
+    @require_numpy1_on_windows
     @require_torch
     @require_pil
     def test_torch_formatter_image(self):
@@ -975,6 +979,7 @@ def test_tf_formatter_sets_default_dtypes(cast_schema, arrow_table):
     tf.debugging.assert_equal(batch["col_float"], tf.ragged.constant(list_float, dtype=tf.float32))
 
 
+@require_numpy1_on_windows
 @require_torch
 @pytest.mark.parametrize(
     "cast_schema",

tests/test_iterable_dataset.py

@@ -51,6 +51,7 @@
     is_rng_equal,
     require_dill_gt_0_3_2,
     require_not_windows,
+    require_numpy1_on_windows,
     require_pyspark,
     require_tf,
     require_torch,
@@ -1279,6 +1280,7 @@ def gen(shard_names):
     assert dataset.n_shards == len(shard_names)
 
 
+@require_numpy1_on_windows
 def test_iterable_dataset_from_file(dataset: IterableDataset, arrow_file: str):
     with assert_arrow_memory_doesnt_increase():
         dataset_from_file = IterableDataset.from_file(arrow_file)

tests/test_py_utils.py

@@ -18,7 +18,7 @@
     zip_dict,
 )
 
-from .utils import require_tf, require_torch
+from .utils import require_numpy1_on_windows, require_tf, require_torch
 
 
 def np_sum(x):  # picklable for multiprocessing
@@ -151,6 +151,7 @@ def gen_random_output():
         np.testing.assert_equal(out1, out2)
         self.assertGreater(np.abs(out1 - out3).sum(), 0)
 
+    @require_numpy1_on_windows
     @require_torch
     def test_torch(self):
         import torch