From 81009b7a5c5cb183a9275c15bf347bdc988b02c4 Mon Sep 17 00:00:00 2001
From: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Date: Tue, 13 Apr 2021 10:33:52 -0400
Subject: [PATCH] Replace error by warning when loading an architecture in
 another (#11207)

* Replace error by warning when loading an architecture in another

* Style

* Style again

* Add a test

* Adapt old test
---
 src/transformers/configuration_utils.py |  9 +++++----
 tests/test_modeling_bert_generation.py  | 13 ++++++-------
 tests/test_modeling_common.py           |  9 +++++----
 3 files changed, 16 insertions(+), 15 deletions(-)

diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py
index ad517ba154..2b08d10b24 100755
--- a/src/transformers/configuration_utils.py
+++ b/src/transformers/configuration_utils.py
@@ -399,10 +399,11 @@ class PretrainedConfig(object):
 
         """
         config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
-        if config_dict.get("model_type", False) and hasattr(cls, "model_type"):
-            assert (
-                config_dict["model_type"] == cls.model_type
-            ), f"You tried to initiate a model of type '{cls.model_type}' with a pretrained model of type '{config_dict['model_type']}'"
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warn(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
 
         return cls.from_dict(config_dict, **kwargs)
 
diff --git a/tests/test_modeling_bert_generation.py b/tests/test_modeling_bert_generation.py
index 2048c127e9..0ca0d81f40 100755
--- a/tests/test_modeling_bert_generation.py
+++ b/tests/test_modeling_bert_generation.py
@@ -231,13 +231,7 @@ class BertGenerationEncoderTester:
         self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
 
     def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        (
-            config,
-            input_ids,
-            input_mask,
-            token_labels,
-        ) = config_and_inputs
+        config, input_ids, input_mask, token_labels = self.prepare_config_and_inputs()
         inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict
 
@@ -259,6 +253,11 @@ class BertGenerationEncoderTest(ModelTesterMixin, GenerationTesterMixin, unittes
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_model_as_bert(self):
+        config, input_ids, input_mask, token_labels = self.model_tester.prepare_config_and_inputs()
+        config.model_type = "bert"
+        self.model_tester.create_and_check_model(config, input_ids, input_mask, token_labels)
+
     def test_model_as_decoder(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py
index d5d76162bc..419b92e280 100755
--- a/tests/test_modeling_common.py
+++ b/tests/test_modeling_common.py
@@ -22,10 +22,10 @@ import tempfile
 import unittest
 from typing import List, Tuple
 
-from transformers import is_torch_available
+from transformers import is_torch_available, logging
 from transformers.file_utils import WEIGHTS_NAME
 from transformers.models.auto import get_values
-from transformers.testing_utils import require_torch, require_torch_multi_gpu, slow, torch_device
+from transformers.testing_utils import CaptureLogger, require_torch, require_torch_multi_gpu, slow, torch_device
 
 
 if is_torch_available():
@@ -1296,6 +1296,7 @@ class ModelUtilsTest(unittest.TestCase):
         model = T5ForConditionalGeneration.from_pretrained(TINY_T5)
         self.assertIsNotNone(model)
 
-        with self.assertRaises(Exception) as context:
+        logger = logging.get_logger("transformers.configuration_utils")
+        with CaptureLogger(logger) as cl:
             BertModel.from_pretrained(TINY_T5)
-        self.assertTrue("You tried to initiate a model of type" in str(context.exception))
+        self.assertTrue("You are using a model of type t5 to instantiate a model of type bert" in cl.out)