[CI] 关闭从hf下载和aistudio下载的单测

tests/transformers/auto/test_confiugration.py

@@ -61,10 +61,12 @@ def test_community_model_class(self):
             auto_config = AutoConfig.from_pretrained(tempdir)
             self.assertEqual(auto_config.hidden_size, number)
 
+    @unittest.skip("skipping due to connection error!")
     def test_from_hf_hub(self):
         config = AutoConfig.from_pretrained("facebook/opt-66b", from_hf_hub=True)
         self.assertEqual(config.hidden_size, 9216)
 
+    @unittest.skip("skipping due to connection error!")
     def test_from_aistudio(self):
         config = AutoConfig.from_pretrained("PaddleNLP/tiny-random-bert", from_aistudio=True)
         self.assertEqual(config.hidden_size, 32)

tests/transformers/auto/test_modeling.py

@@ -67,10 +67,12 @@ def test_model_from_pretrained_cache_dir(self):
             # check against double appending model_name in cache_dir
             self.assertFalse(os.path.exists(os.path.join(tempdir, model_name, model_name)))
 
+    @unittest.skip("skipping due to connection error!")
     def test_from_hf_hub(self):
         model = AutoModel.from_pretrained("PaddleCI/tiny-random-bert", from_hf_hub=True, convert_from_torch=False)
         self.assertIsInstance(model, BertModel)
 
+    @unittest.skip("skipping due to connection error!")
     def test_from_aistudio(self):
         model = AutoModel.from_pretrained("PaddleNLP/tiny-random-bert", from_aistudio=True)
         self.assertIsInstance(model, BertModel)

tests/transformers/auto/test_tokenizer.py

@@ -45,6 +45,7 @@ def test_use_faster(self):
         else:
             self.assertIsInstance(tokenizer, paddlenlp.transformers.BertTokenizer)
 
+    @unittest.skip("skipping due to connection error!")
     def test_hf_tokenizer(self):
         t1 = AutoTokenizer.from_pretrained(
             "hf-internal-testing/tiny-random-BertModel", from_hf_hub=True, use_fast=True
@@ -58,6 +59,7 @@ def test_hf_tokenizer(self):
             self.assertIsInstance(t1, paddlenlp.transformers.BertTokenizer)
         self.assertIsInstance(t2, paddlenlp.transformers.BertTokenizer)
 
+    @unittest.skip("skipping due to connection error!")
     def test_from_aistudio(self):
         tokenizer = AutoTokenizer.from_pretrained("PaddleNLP/tiny-random-bert", from_aistudio=True)
         self.assertIsInstance(tokenizer, paddlenlp.transformers.BertTokenizer)

tests/transformers/load_subfolder/test_config.py

@@ -18,6 +18,7 @@
 from tests.testing_utils import slow
 
 
+@unittest.skip("skipping due to connection error!")
 class ConfigLoadTester(unittest.TestCase):
     @slow
     def test_bert_config_load(self):

tests/transformers/load_subfolder/test_image_processor.py

@@ -19,6 +19,7 @@
 from tests.testing_utils import slow
 
 
+@unittest.skip("skipping due to connection error!")
 class ImageProcessorLoadTester(unittest.TestCase):
     @slow
     def test_clip_load(self):

tests/transformers/load_subfolder/test_model.py

@@ -23,6 +23,7 @@
 from tests.testing_utils import slow
 
 
+@unittest.skip("skipping due to connection error!")
 class ModelLoadTester(unittest.TestCase):
     @pytest.mark.skip
     def test_config_diff(self, config_1, config_2):

tests/transformers/load_subfolder/test_processor.py

@@ -20,6 +20,7 @@
 from tests.testing_utils import slow
 
 
+@unittest.skip("skipping due to connection error!")
 class ProcessorLoadTester(unittest.TestCase):
     @slow
     def test_clip_load(self):

tests/transformers/load_subfolder/test_tokenizer.py

@@ -25,6 +25,7 @@
 from tests.testing_utils import slow
 
 
+@unittest.skip("skipping due to connection error!")
 class TokenizerLoadTester(unittest.TestCase):
     @slow
     def test_bert_load(self):

tests/transformers/test_configuration_utils.py

@@ -164,6 +164,7 @@ def test_from_pretrained_cache_dir(self):
             # check against double appending model_name in cache_dir
             self.assertFalse(os.path.exists(os.path.join(tempdir, model_id, model_id)))
 
+    @unittest.skip("skipping due to connection error!")
     def test_load_from_hf(self):
         """test load config from hf"""
         config = BertConfig.from_pretrained("hf-internal-testing/tiny-random-BertModel", from_hf_hub=True)