Mark big downloads slow (#7325)

* Make big downloads as slow

* Add import

* Right order for slow decorator

* More slow tests

tests/test_data_collator.py

@@ -1,7 +1,7 @@
 import unittest
 
 from transformers import AutoTokenizer, is_torch_available
-from transformers.testing_utils import require_torch
+from transformers.testing_utils import require_torch, slow
 
 
 if is_torch_available():
@@ -69,6 +69,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         self.assertTrue("labels" not in batch)
         self.assertEqual(batch["inputs"].shape, torch.Size([8, 6]))
 
+    @slow
     def test_default_classification(self):
         MODEL_ID = "bert-base-cased-finetuned-mrpc"
         tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
@@ -80,6 +81,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         batch = data_collator(dataset.features)
         self.assertEqual(batch["labels"].dtype, torch.long)
 
+    @slow
     def test_default_regression(self):
         MODEL_ID = "distilroberta-base"
         tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
@@ -91,6 +93,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         batch = data_collator(dataset.features)
         self.assertEqual(batch["labels"].dtype, torch.float)
 
+    @slow
     def test_lm_tokenizer_without_padding(self):
         tokenizer = AutoTokenizer.from_pretrained("gpt2")
         data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)
@@ -109,6 +112,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
         self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
 
+    @slow
     def test_lm_tokenizer_with_padding(self):
         tokenizer = AutoTokenizer.from_pretrained("distilroberta-base")
         data_collator = DataCollatorForLanguageModeling(tokenizer)
@@ -128,6 +132,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512)))
         self.assertEqual(batch["labels"].shape, torch.Size((2, 512)))
 
+    @slow
     def test_plm(self):
         tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased")
         data_collator = DataCollatorForPermutationLanguageModeling(tokenizer)
@@ -156,6 +161,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
             # Expect error due to odd sequence length
             data_collator(example)
 
+    @slow
     def test_nsp(self):
         tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
         data_collator = DataCollatorForNextSentencePrediction(tokenizer)
@@ -172,6 +178,7 @@ class DataCollatorIntegrationTest(unittest.TestCase):
         self.assertEqual(batch["masked_lm_labels"].shape, torch.Size((total_samples, 512)))
         self.assertEqual(batch["next_sentence_label"].shape, torch.Size((total_samples,)))
 
+    @slow
     def test_sop(self):
         tokenizer = AutoTokenizer.from_pretrained("albert-base-v2")
         data_collator = DataCollatorForSOP(tokenizer)