Expose get_config() on ModelTesters (#12812)

* Expose get_config() on ModelTesters

* Typo

tests/test_modeling_flaubert.py

@@ -13,10 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import unittest
 
-from transformers import is_torch_available
+from transformers import FlaubertConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -27,7 +26,6 @@ if is_torch_available():
     import torch
 
     from transformers import (
-        FlaubertConfig,
         FlaubertForMultipleChoice,
         FlaubertForQuestionAnswering,
         FlaubertForQuestionAnsweringSimple,
@@ -96,7 +94,22 @@ class FlaubertModelTester(object):
             is_impossible_labels = ids_tensor([self.batch_size], 2).float()
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = FlaubertConfig(
+        config = self.get_config()
+
+        return (
+            config,
+            input_ids,
+            token_type_ids,
+            input_lengths,
+            sequence_labels,
+            token_labels,
+            is_impossible_labels,
+            choice_labels,
+            input_mask,
+        )
+
+    def get_config(self):
+        return FlaubertConfig(
             vocab_size=self.vocab_size,
             n_special=self.n_special,
             emb_dim=self.hidden_size,
@@ -115,18 +128,6 @@ class FlaubertModelTester(object):
             use_proj=self.use_proj,
         )
 
-        return (
-            config,
-            input_ids,
-            token_type_ids,
-            input_lengths,
-            sequence_labels,
-            token_labels,
-            is_impossible_labels,
-            choice_labels,
-            input_mask,
-        )
-
     def create_and_check_flaubert_model(
         self,
         config,

tests/test_modeling_fsmt.py

@@ -19,7 +19,7 @@ import unittest
 import timeout_decorator  # noqa
 
 from parameterized import parameterized
-from transformers import is_torch_available
+from transformers import FSMTConfig, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -32,7 +32,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import FSMTConfig, FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer
+    from transformers import FSMTForConditionalGeneration, FSMTModel, FSMTTokenizer
     from transformers.models.fsmt.modeling_fsmt import (
         SinusoidalPositionalEmbedding,
         _prepare_fsmt_decoder_inputs,
@@ -42,8 +42,7 @@ if is_torch_available():
     from transformers.pipelines import TranslationPipeline
 
 
-@require_torch
-class ModelTester:
+class FSMTModelTester:
     def __init__(
         self,
         parent,
@@ -78,7 +77,12 @@ class ModelTester:
         )
         input_ids[:, -1] = 2  # Eos Token
 
-        config = FSMTConfig(
+        config = self.get_config()
+        inputs_dict = prepare_fsmt_inputs_dict(config, input_ids)
+        return config, inputs_dict
+
+    def get_config(self):
+        return FSMTConfig(
             vocab_size=self.src_vocab_size,  # hack needed for common tests
             src_vocab_size=self.src_vocab_size,
             tgt_vocab_size=self.tgt_vocab_size,
@@ -97,8 +101,6 @@ class ModelTester:
             bos_token_id=self.bos_token_id,
             pad_token_id=self.pad_token_id,
         )
-        inputs_dict = prepare_fsmt_inputs_dict(config, input_ids)
-        return config, inputs_dict
 
     def prepare_config_and_inputs_for_common(self):
         config, inputs_dict = self.prepare_config_and_inputs()
@@ -141,7 +143,7 @@ class FSMTModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
     test_missing_keys = False
 
     def setUp(self):
-        self.model_tester = ModelTester(self)
+        self.model_tester = FSMTModelTester(self)
         self.langs = ["en", "ru"]
         config = {
             "langs": self.langs,

tests/test_modeling_funnel.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import FunnelTokenizer, is_torch_available
+from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -30,7 +30,6 @@ if is_torch_available():
     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         FunnelBaseModel,
-        FunnelConfig,
         FunnelForMaskedLM,
         FunnelForMultipleChoice,
         FunnelForPreTraining,
@@ -127,7 +126,21 @@ class FunnelModelTester:
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
             fake_token_labels = ids_tensor([self.batch_size, self.seq_length], 1)
 
-        config = FunnelConfig(
+        config = self.get_config()
+
+        return (
+            config,
+            input_ids,
+            token_type_ids,
+            input_mask,
+            sequence_labels,
+            token_labels,
+            choice_labels,
+            fake_token_labels,
+        )
+
+    def get_config(self):
+        return FunnelConfig(
             vocab_size=self.vocab_size,
             block_sizes=self.block_sizes,
             num_decoder_layers=self.num_decoder_layers,
@@ -143,17 +156,6 @@ class FunnelModelTester:
             type_vocab_size=self.type_vocab_size,
         )
 
-        return (
-            config,
-            input_ids,
-            token_type_ids,
-            input_mask,
-            sequence_labels,
-            token_labels,
-            choice_labels,
-            fake_token_labels,
-        )
-
     def create_and_check_model(
         self,
         config,

tests/test_modeling_gpt2.py

@@ -17,7 +17,7 @@
 import datetime
 import unittest
 
-from transformers import is_torch_available
+from transformers import GPT2Config, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -30,7 +30,6 @@ if is_torch_available():
 
     from transformers import (
         GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
-        GPT2Config,
         GPT2DoubleHeadsModel,
         GPT2ForSequenceClassification,
         GPT2LMHeadModel,
@@ -119,25 +118,7 @@ class GPT2ModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = GPT2Config(
-            vocab_size=self.vocab_size,
-            n_embd=self.hidden_size,
-            n_layer=self.num_hidden_layers,
-            n_head=self.num_attention_heads,
-            # intermediate_size=self.intermediate_size,
-            # hidden_act=self.hidden_act,
-            # hidden_dropout_prob=self.hidden_dropout_prob,
-            # attention_probs_dropout_prob=self.attention_probs_dropout_prob,
-            n_positions=self.max_position_embeddings,
-            n_ctx=self.max_position_embeddings,
-            # type_vocab_size=self.type_vocab_size,
-            # initializer_range=self.initializer_range,
-            use_cache=not gradient_checkpointing,
-            bos_token_id=self.bos_token_id,
-            eos_token_id=self.eos_token_id,
-            pad_token_id=self.pad_token_id,
-            gradient_checkpointing=gradient_checkpointing,
-        )
+        config = self.get_config(gradient_checkpointing=gradient_checkpointing)
 
         head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
 
@@ -153,6 +134,27 @@ class GPT2ModelTester:
             choice_labels,
         )
 
+    def get_config(self, gradient_checkpointing=False):
+        return GPT2Config(
+            vocab_size=self.vocab_size,
+            n_embd=self.hidden_size,
+            n_layer=self.num_hidden_layers,
+            n_head=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            n_positions=self.max_position_embeddings,
+            n_ctx=self.max_position_embeddings,
+            type_vocab_size=self.type_vocab_size,
+            initializer_range=self.initializer_range,
+            use_cache=not gradient_checkpointing,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+            gradient_checkpointing=gradient_checkpointing,
+        )
+
     def prepare_config_and_inputs_for_decoder(self):
         (
             config,

tests/test_modeling_gpt_neo.py

@@ -17,7 +17,7 @@
 
 import unittest
 
-from transformers import is_torch_available
+from transformers import GPTNeoConfig, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_torch, slow, torch_device
 
@@ -32,7 +32,6 @@ if is_torch_available():
     from transformers import (
         GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
         GPT2Tokenizer,
-        GPTNeoConfig,
         GPTNeoForCausalLM,
         GPTNeoForSequenceClassification,
         GPTNeoModel,
@@ -123,20 +122,7 @@ class GPTNeoModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = GPTNeoConfig(
-            vocab_size=self.vocab_size,
-            hidden_size=self.hidden_size,
-            num_layers=self.num_hidden_layers,
-            num_heads=self.num_attention_heads,
-            max_position_embeddings=self.max_position_embeddings,
-            use_cache=not gradient_checkpointing,
-            bos_token_id=self.bos_token_id,
-            eos_token_id=self.eos_token_id,
-            pad_token_id=self.pad_token_id,
-            gradient_checkpointing=gradient_checkpointing,
-            window_size=self.window_size,
-            attention_types=self.attention_types,
-        )
+        config = self.get_config(gradient_checkpointing=False)
 
         head_mask = ids_tensor([self.num_hidden_layers, self.num_attention_heads], 2)
 
@@ -152,6 +138,22 @@ class GPTNeoModelTester:
             choice_labels,
         )
 
+    def get_config(self, gradient_checkpointing=False):
+        return GPTNeoConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_hidden_layers,
+            num_heads=self.num_attention_heads,
+            max_position_embeddings=self.max_position_embeddings,
+            use_cache=not gradient_checkpointing,
+            bos_token_id=self.bos_token_id,
+            eos_token_id=self.eos_token_id,
+            pad_token_id=self.pad_token_id,
+            gradient_checkpointing=gradient_checkpointing,
+            window_size=self.window_size,
+            attention_types=self.attention_types,
+        )
+
     def prepare_config_and_inputs_for_decoder(self):
         (
             config,

tests/test_modeling_hubert.py

@@ -21,7 +21,7 @@ import unittest
 import pytest
 
 from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
-from transformers import is_torch_available
+from transformers import HubertConfig, is_torch_available
 from transformers.testing_utils import require_datasets, require_soundfile, require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -31,7 +31,7 @@ from .test_modeling_common import ModelTesterMixin, _config_zero_init
 if is_torch_available():
     import torch
 
-    from transformers import HubertConfig, HubertForCTC, HubertModel, Wav2Vec2Processor
+    from transformers import HubertForCTC, HubertModel, Wav2Vec2Processor
     from transformers.models.hubert.modeling_hubert import _compute_mask_indices
 
 
@@ -98,7 +98,12 @@ class HubertModelTester:
         input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
         attention_mask = random_attention_mask([self.batch_size, self.seq_length])
 
-        config = HubertConfig(
+        config = self.get_config()
+
+        return config, input_values, attention_mask
+
+    def get_config(self):
+        return HubertConfig(
             hidden_size=self.hidden_size,
             feat_extract_norm=self.feat_extract_norm,
             feat_extract_dropout=self.feat_extract_dropout,
@@ -119,8 +124,6 @@ class HubertModelTester:
             vocab_size=self.vocab_size,
         )
 
-        return config, input_values, attention_mask
-
     def create_and_check_model(self, config, input_values, attention_mask):
         model = HubertModel(config=config)
         model.to(torch_device)

tests/test_modeling_ibert.py

@@ -17,7 +17,7 @@
 import copy
 import unittest
 
-from transformers import is_torch_available
+from transformers import IBertConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -30,7 +30,6 @@ if is_torch_available():
 
     from transformers import (
         IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
-        IBertConfig,
         IBertForMaskedLM,
         IBertForMultipleChoice,
         IBertForQuestionAnswering,
@@ -97,7 +96,12 @@ class IBertModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = IBertConfig(
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return IBertConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_hidden_layers=self.num_hidden_layers,
@@ -112,8 +116,6 @@ class IBertModelTester:
             quant_mode=True,
         )
 
-        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
     def create_and_check_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):

tests/test_modeling_layoutlm.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import is_torch_available
+from transformers import LayoutLMConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -27,7 +27,6 @@ if is_torch_available():
     import torch
 
     from transformers import (
-        LayoutLMConfig,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
@@ -120,7 +119,12 @@ class LayoutLMModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = LayoutLMConfig(
+        config = self.get_config()
+
+        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return LayoutLMConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_hidden_layers=self.num_hidden_layers,
@@ -134,8 +138,6 @@ class LayoutLMModelTester:
             initializer_range=self.initializer_range,
         )
 
-        return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
     def create_and_check_model(
         self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):

tests/test_modeling_led.py

@@ -19,7 +19,7 @@ import copy
 import tempfile
 import unittest
 
-from transformers import is_torch_available
+from transformers import LEDConfig, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
@@ -34,7 +34,6 @@ if is_torch_available():
 
     from transformers import (
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
-        LEDConfig,
         LEDForConditionalGeneration,
         LEDForQuestionAnswering,
         LEDForSequenceClassification,
@@ -75,7 +74,6 @@ def prepare_led_inputs_dict(
     }
 
 
-@require_torch
 class LEDModelTester:
     def __init__(
         self,
@@ -141,7 +139,12 @@ class LEDModelTester:
 
         decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
-        config = LEDConfig(
+        config = self.get_config()
+        inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids)
+        return config, inputs_dict
+
+    def get_config(self):
+        return LEDConfig(
             vocab_size=self.vocab_size,
             d_model=self.hidden_size,
             encoder_layers=self.num_hidden_layers,
@@ -158,8 +161,6 @@ class LEDModelTester:
             pad_token_id=self.pad_token_id,
             attention_window=self.attention_window,
         )
-        inputs_dict = prepare_led_inputs_dict(config, input_ids, decoder_input_ids)
-        return config, inputs_dict
 
     def prepare_config_and_inputs_for_common(self):
         config, inputs_dict = self.prepare_config_and_inputs()

tests/test_modeling_longformer.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import is_torch_available
+from transformers import LongformerConfig, is_torch_available
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -27,7 +27,6 @@ if is_torch_available():
     import torch
 
     from transformers import (
-        LongformerConfig,
         LongformerForMaskedLM,
         LongformerForMultipleChoice,
         LongformerForQuestionAnswering,
@@ -100,7 +99,12 @@ class LongformerModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = LongformerConfig(
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return LongformerConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_hidden_layers=self.num_hidden_layers,
@@ -115,8 +119,6 @@ class LongformerModelTester:
             attention_window=self.attention_window,
         )
 
-        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
     def create_and_check_attention_mask_determinism(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):

tests/test_modeling_luke.py

@@ -13,10 +13,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """ Testing suite for the PyTorch LUKE model. """
-
 import unittest
 
-from transformers import is_torch_available
+from transformers import LukeConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -27,7 +26,6 @@ if is_torch_available():
     import torch
 
     from transformers import (
-        LukeConfig,
         LukeForEntityClassification,
         LukeForEntityPairClassification,
         LukeForEntitySpanClassification,
@@ -154,7 +152,25 @@ class LukeModelTester:
                 [self.batch_size, self.entity_length], self.num_entity_span_classification_labels
             )
 
-        config = LukeConfig(
+        config = self.get_config()
+
+        return (
+            config,
+            input_ids,
+            attention_mask,
+            token_type_ids,
+            entity_ids,
+            entity_attention_mask,
+            entity_token_type_ids,
+            entity_position_ids,
+            sequence_labels,
+            entity_classification_labels,
+            entity_pair_classification_labels,
+            entity_span_classification_labels,
+        )
+
+    def get_config(self):
+        return LukeConfig(
             vocab_size=self.vocab_size,
             entity_vocab_size=self.entity_vocab_size,
             entity_emb_size=self.entity_emb_size,
@@ -172,21 +188,6 @@ class LukeModelTester:
             use_entity_aware_attention=self.use_entity_aware_attention,
         )
 
-        return (
-            config,
-            input_ids,
-            attention_mask,
-            token_type_ids,
-            entity_ids,
-            entity_attention_mask,
-            entity_token_type_ids,
-            entity_position_ids,
-            sequence_labels,
-            entity_classification_labels,
-            entity_pair_classification_labels,
-            entity_span_classification_labels,
-        )
-
     def create_and_check_model(
         self,
         config,

tests/test_modeling_lxmert.py

@@ -19,7 +19,7 @@ import unittest
 
 import numpy as np
 
-from transformers import is_torch_available
+from transformers import LxmertConfig, is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_torch, slow, torch_device
 
@@ -33,7 +33,6 @@ if is_torch_available():
     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
-        LxmertConfig,
         LxmertForPreTraining,
         LxmertForQuestionAnswering,
         LxmertModel,
@@ -170,7 +169,24 @@ class LxmertModelTester:
         if self.task_matched:
             matched_label = ids_tensor([self.batch_size], self.num_labels)
 
-        config = LxmertConfig(
+        config = self.get_config()
+
+        return (
+            config,
+            input_ids,
+            visual_feats,
+            bounding_boxes,
+            token_type_ids,
+            input_mask,
+            obj_labels,
+            masked_lm_labels,
+            matched_label,
+            ans,
+            output_attentions,
+        )
+
+    def get_config(self):
+        return LxmertConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_attention_heads=self.num_attention_heads,
@@ -204,20 +220,6 @@ class LxmertModelTester:
             output_hidden_states=self.output_hidden_states,
         )
 
-        return (
-            config,
-            input_ids,
-            visual_feats,
-            bounding_boxes,
-            token_type_ids,
-            input_mask,
-            obj_labels,
-            masked_lm_labels,
-            matched_label,
-            ans,
-            output_attentions,
-        )
-
     def create_and_check_lxmert_model(
         self,
         config,

tests/test_modeling_m2m_100.py

@@ -19,7 +19,7 @@ import copy
 import tempfile
 import unittest
 
-from transformers import is_torch_available
+from transformers import M2M100Config, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -31,7 +31,7 @@ from .test_modeling_common import ModelTesterMixin, ids_tensor
 if is_torch_available():
     import torch
 
-    from transformers import M2M100Config, M2M100ForConditionalGeneration, M2M100Model, M2M100Tokenizer
+    from transformers import M2M100ForConditionalGeneration, M2M100Model, M2M100Tokenizer
     from transformers.models.m2m_100.modeling_m2m_100 import M2M100Decoder, M2M100Encoder
 
 
@@ -66,7 +66,6 @@ def prepare_m2m_100_inputs_dict(
     }
 
 
-@require_torch
 class M2M100ModelTester:
     def __init__(
         self,
@@ -125,7 +124,12 @@ class M2M100ModelTester:
         input_ids = input_ids.clamp(self.pad_token_id + 1)
         decoder_input_ids = decoder_input_ids.clamp(self.pad_token_id + 1)
 
-        config = M2M100Config(
+        config = self.get_config()
+        inputs_dict = prepare_m2m_100_inputs_dict(config, input_ids, decoder_input_ids)
+        return config, inputs_dict
+
+    def get_config(self):
+        return M2M100Config(
             vocab_size=self.vocab_size,
             d_model=self.hidden_size,
             encoder_layers=self.num_hidden_layers,
@@ -143,8 +147,6 @@ class M2M100ModelTester:
             bos_token_id=self.bos_token_id,
             pad_token_id=self.pad_token_id,
         )
-        inputs_dict = prepare_m2m_100_inputs_dict(config, input_ids, decoder_input_ids)
-        return config, inputs_dict
 
     def prepare_config_and_inputs_for_common(self):
         config, inputs_dict = self.prepare_config_and_inputs()

tests/test_modeling_marian.py

@@ -17,7 +17,7 @@
 import tempfile
 import unittest
 
-from transformers import is_torch_available
+from transformers import MarianConfig, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.hf_api import HfApi
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
@@ -34,7 +34,6 @@ if is_torch_available():
         AutoConfig,
         AutoModelWithLMHead,
         AutoTokenizer,
-        MarianConfig,
         MarianModel,
         MarianMTModel,
         TranslationPipeline,
@@ -83,7 +82,6 @@ def prepare_marian_inputs_dict(
     }
 
 
-@require_torch
 class MarianModelTester:
     def __init__(
         self,
@@ -126,7 +124,6 @@ class MarianModelTester:
         self.decoder_start_token_id = decoder_start_token_id
 
     def prepare_config_and_inputs(self):
-        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
             3,
         )
@@ -134,7 +131,12 @@ class MarianModelTester:
 
         decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
-        config = MarianConfig(
+        config = self.get_config()
+        inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids)
+        return config, inputs_dict
+
+    def get_config(self):
+        return MarianConfig(
             vocab_size=self.vocab_size,
             d_model=self.hidden_size,
             encoder_layers=self.num_hidden_layers,
@@ -151,8 +153,6 @@ class MarianModelTester:
             pad_token_id=self.pad_token_id,
             decoder_start_token_id=self.decoder_start_token_id,
         )
-        inputs_dict = prepare_marian_inputs_dict(config, input_ids, decoder_input_ids)
-        return config, inputs_dict
 
     def prepare_config_and_inputs_for_common(self):
         config, inputs_dict = self.prepare_config_and_inputs()

tests/test_modeling_mbart.py

@@ -19,7 +19,7 @@ import copy
 import tempfile
 import unittest
 
-from transformers import is_torch_available
+from transformers import MBartConfig, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -34,7 +34,6 @@ if is_torch_available():
     from transformers import (
         AutoTokenizer,
         BatchEncoding,
-        MBartConfig,
         MBartForCausalLM,
         MBartForConditionalGeneration,
         MBartForQuestionAnswering,
@@ -75,7 +74,6 @@ def prepare_mbart_inputs_dict(
     }
 
 
-@require_torch
 class MBartModelTester:
     def __init__(
         self,
@@ -124,7 +122,12 @@ class MBartModelTester:
 
         decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
-        config = MBartConfig(
+        config = self.get_config()
+        inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
+        return config, inputs_dict
+
+    def get_config(self):
+        return MBartConfig(
             vocab_size=self.vocab_size,
             d_model=self.hidden_size,
             encoder_layers=self.num_hidden_layers,
@@ -140,8 +143,6 @@ class MBartModelTester:
             bos_token_id=self.bos_token_id,
             pad_token_id=self.pad_token_id,
         )
-        inputs_dict = prepare_mbart_inputs_dict(config, input_ids, decoder_input_ids)
-        return config, inputs_dict
 
     def prepare_config_and_inputs_for_common(self):
         config, inputs_dict = self.prepare_config_and_inputs()

tests/test_modeling_megatron_bert.py

@@ -19,7 +19,7 @@ import math
 import os
 import unittest
 
-from transformers import is_torch_available
+from transformers import MegatronBertConfig, is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -32,7 +32,6 @@ if is_torch_available():
 
     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
-        MegatronBertConfig,
         MegatronBertForCausalLM,
         MegatronBertForMaskedLM,
         MegatronBertForMultipleChoice,
@@ -115,7 +114,12 @@ class MegatronBertModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = MegatronBertConfig(
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return MegatronBertConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_hidden_layers=self.num_hidden_layers,
@@ -131,8 +135,6 @@ class MegatronBertModelTester:
             initializer_range=self.initializer_range,
         )
 
-        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
     def create_and_check_megatron_bert_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):

tests/test_modeling_mobilebert.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import is_torch_available
+from transformers import MobileBertConfig, is_torch_available
 from transformers.models.auto import get_values
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -29,7 +29,6 @@ if is_torch_available():
 
     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
-        MobileBertConfig,
         MobileBertForMaskedLM,
         MobileBertForMultipleChoice,
         MobileBertForNextSentencePrediction,
@@ -111,7 +110,12 @@ class MobileBertModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = MobileBertConfig(
+        config = self.get_config()
+
+        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return MobileBertConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_hidden_layers=self.num_hidden_layers,
@@ -127,8 +131,6 @@ class MobileBertModelTester:
             initializer_range=self.initializer_range,
         )
 
-        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
     def create_and_check_mobilebert_model(
         self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
     ):

tests/test_modeling_mpnet.py

@@ -16,7 +16,7 @@
 
 import unittest
 
-from transformers import is_torch_available
+from transformers import MPNetConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -27,7 +27,6 @@ if is_torch_available():
     import torch
 
     from transformers import (
-        MPNetConfig,
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
@@ -104,7 +103,11 @@ class MPNetModelTester:
             token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
             choice_labels = ids_tensor([self.batch_size], self.num_choices)
 
-        config = MPNetConfig(
+        config = self.get_config()
+        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
+
+    def get_config(self):
+        return MPNetConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_hidden_layers=self.num_hidden_layers,
@@ -116,7 +119,6 @@ class MPNetModelTester:
             max_position_embeddings=self.max_position_embeddings,
             initializer_range=self.initializer_range,
         )
-        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
 
     def create_and_check_mpnet_model(
         self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

tests/test_modeling_pegasus.py

@@ -17,7 +17,7 @@
 import tempfile
 import unittest
 
-from transformers import is_torch_available
+from transformers import PegasusConfig, is_torch_available
 from transformers.file_utils import cached_property
 from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
 
@@ -30,7 +30,7 @@ from .test_modeling_mbart import AbstractSeq2SeqIntegrationTest
 if is_torch_available():
     import torch
 
-    from transformers import AutoModelForSeq2SeqLM, PegasusConfig, PegasusForConditionalGeneration, PegasusModel
+    from transformers import AutoModelForSeq2SeqLM, PegasusForConditionalGeneration, PegasusModel
     from transformers.models.pegasus.modeling_pegasus import PegasusDecoder, PegasusEncoder, PegasusForCausalLM
 
 
@@ -65,7 +65,6 @@ def prepare_pegasus_inputs_dict(
     }
 
 
-@require_torch
 class PegasusModelTester:
     def __init__(
         self,
@@ -114,7 +113,12 @@ class PegasusModelTester:
 
         decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
-        config = PegasusConfig(
+        config = self.get_config()
+        inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
+        return config, inputs_dict
+
+    def get_config(self):
+        return PegasusConfig(
             vocab_size=self.vocab_size,
             d_model=self.hidden_size,
             encoder_layers=self.num_hidden_layers,
@@ -130,8 +134,6 @@ class PegasusModelTester:
             bos_token_id=self.bos_token_id,
             pad_token_id=self.pad_token_id,
         )
-        inputs_dict = prepare_pegasus_inputs_dict(config, input_ids, decoder_input_ids)
-        return config, inputs_dict
 
     def prepare_config_and_inputs_for_common(self):
         config, inputs_dict = self.prepare_config_and_inputs()

tests/test_modeling_prophetnet.py

@@ -13,12 +13,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 import copy
 import tempfile
 import unittest
 
-from transformers import is_torch_available
+from transformers import ProphetNetConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
 
 from .test_configuration_common import ConfigTester
@@ -30,7 +29,6 @@ if is_torch_available():
     import torch
 
     from transformers import (
-        ProphetNetConfig,
         ProphetNetDecoder,
         ProphetNetEncoder,
         ProphetNetForCausalLM,
@@ -124,7 +122,19 @@ class ProphetNetModelTester:
         if self.use_labels:
             lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
 
-        config = ProphetNetConfig(
+        config = self.get_config()
+
+        return (
+            config,
+            input_ids,
+            decoder_input_ids,
+            attention_mask,
+            decoder_attention_mask,
+            lm_labels,
+        )
+
+    def get_config(self):
+        return ProphetNetConfig(
             vocab_size=self.vocab_size,
             hidden_size=self.hidden_size,
             num_encoder_layers=self.num_encoder_layers,
@@ -145,15 +155,6 @@ class ProphetNetModelTester:
             is_encoder_decoder=self.is_encoder_decoder,
         )
 
-        return (
-            config,
-            input_ids,
-            decoder_input_ids,
-            attention_mask,
-            decoder_attention_mask,
-            lm_labels,
-        )
-
     def prepare_config_and_inputs_for_decoder(self):
         (
             config,