Remove repeated test_model_from_pretrained, test_model, test_config

tests/models/albert/test_modeling_albert.py

@@ -267,6 +267,7 @@ class AlbertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else {}
     )
     fx_compatible = True
+    pretrained_checkpoint = "albert/albert-base-v1"
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
@@ -286,13 +287,6 @@ def setUp(self):
         self.model_tester = AlbertModelTester(self)
         self.config_tester = ConfigTester(self, config_class=AlbertConfig, hidden_size=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     def test_for_pretraining(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
@@ -319,12 +313,6 @@ def test_model_various_embeddings(self):
             config_and_inputs[0].position_embedding_type = type
             self.model_tester.create_and_check_model(*config_and_inputs)
 
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "albert/albert-base-v1"
-        model = AlbertModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 @require_torch
 class AlbertModelIntegrationTest(unittest.TestCase):

tests/models/albert/test_modeling_flax_albert.py

@@ -132,17 +132,11 @@ class FlaxAlbertModelTest(FlaxModelTesterMixin, unittest.TestCase):
         if is_flax_available()
         else ()
     )
+    pretrained_checkpoint = "albert/albert-base-v2"
 
     def setUp(self):
         self.model_tester = FlaxAlbertModelTester(self)
 
-    @slow
-    def test_model_from_pretrained(self):
-        for model_class_name in self.all_model_classes:
-            model = model_class_name.from_pretrained("albert/albert-base-v2")
-            outputs = model(np.ones((1, 1)))
-            self.assertIsNotNone(outputs)
-
 
 @require_flax
 class FlaxAlbertModelIntegrationTest(unittest.TestCase):

tests/models/align/test_modeling_align.py

@@ -138,6 +138,7 @@ class AlignVisionModelTest(ModelTesterMixin, unittest.TestCase):
     test_resize_embeddings = False
     test_head_masking = False
     has_attentions = False
+    pretrained_checkpoint = "kakaobrain/align-base"
 
     def setUp(self):
         self.model_tester = AlignVisionModelTester(self)
@@ -149,9 +150,6 @@ def setUp(self):
             common_properties=["num_channels", "image_size"],
         )
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     @unittest.skip(reason="AlignVisionModel does not use inputs_embeds")
     def test_inputs_embeds(self):
         pass
@@ -176,10 +174,6 @@ def test_forward_signature(self):
             expected_arg_names = ["pixel_values"]
             self.assertListEqual(arg_names[:1], expected_arg_names)
 
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     def test_hidden_states_output(self):
         def check_hidden_states_output(inputs_dict, config, model_class):
             model = model_class(config)
@@ -230,12 +224,6 @@ def test_training_gradient_checkpointing_use_reentrant(self):
     def test_training_gradient_checkpointing_use_reentrant_false(self):
         pass
 
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "kakaobrain/align-base"
-        model = AlignVisionModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 class AlignTextModelTester:
     def __init__(
@@ -340,18 +328,12 @@ class AlignTextModelTest(ModelTesterMixin, unittest.TestCase):
     fx_compatible = False
     test_pruning = False
     test_head_masking = False
+    pretrained_checkpoint = "kakaobrain/align-base"
 
     def setUp(self):
         self.model_tester = AlignTextModelTester(self)
         self.config_tester = ConfigTester(self, config_class=AlignTextConfig, hidden_size=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     @unittest.skip
     def test_training(self):
         pass
@@ -388,12 +370,6 @@ def test_save_load_fast_init_from_base(self):
     def test_save_load_fast_init_to_base(self):
         pass
 
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "kakaobrain/align-base"
-        model = AlignTextModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 class AlignModelTester:
     def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
@@ -454,14 +430,11 @@ class AlignModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     test_pruning = False
     test_resize_embeddings = False
     test_attention_outputs = False
+    pretrained_checkpoint = "kakaobrain/align-base"
 
     def setUp(self):
         self.model_tester = AlignModelTester(self)
 
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     @unittest.skip(reason="Start to fail after using torch `cu118`.")
     def test_multi_gpu_data_parallel_forward(self):
         super().test_multi_gpu_data_parallel_forward()
@@ -601,12 +574,6 @@ def test_load_vision_text_config(self):
             text_config = AlignTextConfig.from_pretrained(tmp_dir_name)
             self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
 
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "kakaobrain/align-base"
-        model = AlignModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 # We will verify our results on an image of cute cats
 def prepare_img():

tests/models/altclip/test_modeling_altclip.py

@@ -146,9 +146,6 @@ def setUp(self):
             self, config_class=AltCLIPVisionConfig, has_text_modality=False, hidden_size=37
         )
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     @unittest.skip(reason="CLIP does not use inputs_embeds")
     def test_inputs_embeds(self):
         pass
@@ -174,10 +171,6 @@ def test_forward_signature(self):
             expected_arg_names = ["pixel_values"]
             self.assertListEqual(arg_names[:1], expected_arg_names)
 
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     @unittest.skip
     def test_training(self):
         pass
@@ -309,6 +302,7 @@ class AltCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
     fx_compatible = True
     test_pruning = False
     test_head_masking = False
+    pretrained_checkpoint = "BAAI/AltCLIP"
 
     # TODO (@SunMarc): Fix me
     @unittest.skip(reason="It's broken.")
@@ -319,13 +313,6 @@ def setUp(self):
         self.model_tester = AltCLIPTextModelTester(self)
         self.config_tester = ConfigTester(self, config_class=AltCLIPTextConfig, hidden_size=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     @unittest.skip
     def test_training(self):
         pass
@@ -365,12 +352,6 @@ def test_save_load_fast_init_from_base(self):
     def test_save_load_fast_init_to_base(self):
         pass
 
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "BAAI/AltCLIP"
-        model = AltCLIPTextModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 class AltCLIPModelTester:
     def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
@@ -433,6 +414,7 @@ class AltCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
     test_pruning = False
     test_resize_embeddings = False
     test_attention_outputs = False
+    pretrained_checkpoint = "BAAI/AltCLIP"
 
     # TODO: Fix the failed tests when this model gets more usage
     def is_pipeline_test_to_skip(
@@ -446,10 +428,6 @@ def is_pipeline_test_to_skip(
     def setUp(self):
         self.model_tester = AltCLIPModelTester(self)
 
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
     @unittest.skip(reason="Hidden_states is tested in individual model tests")
     def test_hidden_states_output(self):
         pass
@@ -560,12 +538,6 @@ def _create_and_check_torchscript(self, config, inputs_dict):
 
             self.assertTrue(models_equal)
 
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "BAAI/AltCLIP"
-        model = AltCLIPModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 @require_vision
 @require_torch

tests/models/audio_spectrogram_transformer/test_modeling_audio_spectrogram_transformer.py

@@ -164,6 +164,7 @@ class ASTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     test_pruning = False
     test_resize_embeddings = False
     test_head_masking = False
+    pretrained_checkpoint = "MIT/ast-finetuned-audioset-10-10-0.4593"
 
     # TODO: Fix the failed tests when this model gets more usage
     def is_pipeline_test_to_skip(
@@ -178,9 +179,6 @@ def setUp(self):
         self.model_tester = ASTModelTester(self)
         self.config_tester = ConfigTester(self, config_class=ASTConfig, has_text_modality=False, hidden_size=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     @unittest.skip(reason="AST does not use inputs_embeds")
     def test_inputs_embeds(self):
         pass
@@ -206,16 +204,6 @@ def test_forward_signature(self):
             expected_arg_names = ["input_values"]
             self.assertListEqual(arg_names[:1], expected_arg_names)
 
-    def test_model(self):
-        config_and_inputs = self.model_tester.prepare_config_and_inputs()
-        self.model_tester.create_and_check_model(*config_and_inputs)
-
-    @slow
-    def test_model_from_pretrained(self):
-        model_name = "MIT/ast-finetuned-audioset-10-10-0.4593"
-        model = ASTModel.from_pretrained(model_name)
-        self.assertIsNotNone(model)
-
 
 # We will verify our results on some audio from AudioSet
 def prepare_audio():

tests/models/autoformer/test_modeling_autoformer.py

@@ -110,6 +110,23 @@ def get_config(self):
             scaling="std",  # we need std to get non-zero `loc`
         )
 
+    def create_and_check_model(self, config, input_values):
+        model = AutoformerModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(**input_values)
+
+        past_time_features = input_values["past_time_features"]
+        future_time_features = input_values["future_time_features"]
+        past_length = config.context_length + max(config.lags_sequence)
+        time_feat = torch.cat(
+            [past_time_features[:, past_length - config.context_length :, ...], future_time_features], dim=1
+        )
+        sequence_length = time_feat.shape[1] - (config.context_length - config.label_length)
+        self.parent.assertEqual(
+            tuple(result.last_hidden_state.shape), (self.batch_size, sequence_length, self.hidden_size)
+        )
+
     def prepare_autoformer_inputs_dict(self, config):
         _past_length = config.context_length + max(config.lags_sequence)
 
@@ -217,9 +234,6 @@ def setUp(self):
         self.model_tester = AutoformerModelTester(self)
         self.config_tester = ConfigTester(self, config_class=AutoformerConfig, has_text_modality=False)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     def test_save_load_strict(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:

tests/models/bark/test_modeling_bark.py

@@ -542,9 +542,6 @@ def setUp(self):
         self.model_tester = BarkSemanticModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BarkSemanticConfig, n_embd=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     def test_save_load_strict(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
@@ -631,9 +628,6 @@ def setUp(self):
         self.model_tester = BarkCoarseModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BarkCoarseConfig, n_embd=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     def test_save_load_strict(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
@@ -721,9 +715,6 @@ def setUp(self):
         self.model_tester = BarkFineModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BarkFineConfig, n_embd=37)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     def test_save_load_strict(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:

tests/models/bart/test_modeling_bart.py

@@ -443,9 +443,6 @@ def setUp(self):
         self.model_tester = BartModelTester(self)
         self.config_tester = ConfigTester(self, config_class=BartConfig)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     def test_save_load_strict(self):
         config, inputs_dict = self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
@@ -1514,9 +1511,6 @@ def setUp(
         self.model_tester = BartStandaloneDecoderModelTester(self, is_training=False)
         self.config_tester = ConfigTester(self, config_class=BartConfig)
 
-    def test_config(self):
-        self.config_tester.run_common_tests()
-
     def test_decoder_model_past(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past(*config_and_inputs)