Update tiny model creation script (#22202)

* Update UNCONVERTIBLE_MODEL_ARCHITECTURES

* Deal with 2 model tester classes in single test file

* Deal with 2 model tester classes in single test file

* Deal with 2 model tester classes in single test file

* make style and quality

---------
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

utils/create_dummy_models.py

@@ -16,18 +16,17 @@
 import argparse
 import collections.abc
 import copy
-import importlib
 import inspect
 import json
 import os
 import shutil
-import sys
 import tempfile
 import traceback
 from pathlib import Path
 
 from check_config_docstrings import get_checkpoint_from_config_class
 from datasets import load_dataset
+from get_test_info import get_model_to_tester_mapping, get_tester_classes_for_model
 from huggingface_hub import Repository, create_repo, upload_folder
 
 from transformers import (
@@ -58,7 +57,6 @@ logging.set_verbosity_error()
 logging.disable_progress_bar()
 logger = logging.get_logger(__name__)
 
-sys.path.append(".")
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 if not is_torch_available():
@@ -67,6 +65,7 @@ if not is_torch_available():
 if not is_tf_available():
     raise ValueError("Please install TensorFlow.")
 
+
 FRAMEWORKS = ["pytorch", "tensorflow"]
 INVALID_ARCH = []
 TARGET_VOCAB_SIZE = 1024
@@ -94,8 +93,12 @@ UNCONVERTIBLE_MODEL_ARCHITECTURES = {
     "TFCamembertModel",
     "TFCamembertForCausalLM",
     "DecisionTransformerModel",
+    "GraphormerModel",
+    "InformerModel",
     "JukeboxModel",
     "MarianForCausalLM",
+    "MaskFormerSwinModel",
+    "MaskFormerSwinBackbone",
     "MT5Model",
     "MT5ForConditionalGeneration",
     "TFMT5ForConditionalGeneration",
@@ -126,6 +129,7 @@ UNCONVERTIBLE_MODEL_ARCHITECTURES = {
     "XLMRobertaForQuestionAnswering",
     "TFXLMRobertaForSequenceClassification",
     "TFXLMRobertaForMaskedLM",
+    "TFXLMRobertaForCausalLM",
     "TFXLMRobertaForQuestionAnswering",
     "TFXLMRobertaModel",
     "TFXLMRobertaForMultipleChoice",
@@ -355,7 +359,7 @@ def build_processor(config_class, processor_class, allow_no_checkpoint=False):
     return processor
 
 
-def get_tiny_config(config_class, **model_tester_kwargs):
+def get_tiny_config(config_class, model_class=None, **model_tester_kwargs):
     """Retrieve a tiny configuration from `config_class` using each model's `ModelTester`.
 
     Args:
@@ -378,9 +382,18 @@ def get_tiny_config(config_class, **model_tester_kwargs):
         module_name = model_type_to_module_name(model_type)
         if not modeling_name.startswith(module_name):
             raise ValueError(f"{modeling_name} doesn't start with {module_name}!")
-        module = importlib.import_module(f".models.{module_name}.test_modeling_{modeling_name}", package="tests")
-        camel_case_model_name = config_class.__name__.split("Config")[0]
-        model_tester_class = getattr(module, f"{camel_case_model_name}ModelTester", None)
+        test_file = os.path.join("tests", "models", module_name, f"test_modeling_{modeling_name}.py")
+        models_to_model_testers = get_model_to_tester_mapping(test_file)
+        # Find the model tester class
+        model_tester_class = None
+        tester_classes = []
+        if model_class is not None:
+            tester_classes = get_tester_classes_for_model(test_file, model_class)
+        else:
+            for _tester_classes in models_to_model_testers.values():
+                tester_classes.extend(_tester_classes)
+        if len(tester_classes) > 0:
+            model_tester_class = sorted(tester_classes, key=lambda x: x.__name__)[0]
     except ModuleNotFoundError:
         error = f"Tiny config not created for {model_type} - cannot find the testing module from the model name."
         raise ValueError(error)