revert unnecessary changes

.github/workflows/test_onnxruntime.yml

@@ -17,8 +17,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-20.04, windows-2019, macos-15]
         transformers-version: ["latest"]
+        os: [ubuntu-20.04, windows-2019, macos-13]
         include:
           - transformers-version: "4.45.*"
             os: ubuntu-20.04

tests/onnxruntime/test_modeling.py

@@ -24,7 +24,6 @@
 import numpy as np
 import onnx
 import onnxruntime
-import onnxruntime as ort
 import pytest
 import requests
 import timm
@@ -99,7 +98,6 @@
 )
 from optimum.onnxruntime.base import ORTDecoderForSeq2Seq, ORTEncoder
 from optimum.onnxruntime.modeling_ort import ORTModel
-from optimum.onnxruntime.utils import get_device_for_provider, get_provider_for_device
 from optimum.pipelines import pipeline
 from optimum.utils import (
     CONFIG_NAME,
@@ -5722,23 +5720,3 @@ def test_find_untested_architectures(self, task: str, test_class):
                 f"For the task `{task}`, the ONNX export supports {supported_export_models}, but only {tested_architectures} are tested.\n"
                 f"    Missing {untested_architectures}."
             )
-
-
-class ProviderAndDeviceGettersTest(unittest.TestCase):
-    def test_get_device_for_provider(self):
-        self.assertEqual(
-            get_device_for_provider("CPUExecutionProvider", provider_options={}),
-            torch.device("cpu"),
-        )
-        self.assertEqual(
-            get_device_for_provider("CUDAExecutionProvider", provider_options={"device_id": 1}),
-            torch.device("cuda:1"),
-        )
-
-    def test_get_provider_for_device(self):
-        self.assertEqual(get_provider_for_device(torch.device("cpu")), "CPUExecutionProvider")
-
-        if "ROCMExecutionProvider" in ort.get_available_providers():
-            self.assertEqual(get_provider_for_device(torch.device("cuda")), "ROCMExecutionProvider")
-        else:
-            self.assertEqual(get_provider_for_device(torch.device("cuda")), "CUDAExecutionProvider")

tests/onnxruntime/test_utils.py

@@ -0,0 +1,35 @@
+import tempfile
+import unittest
+
+import onnxruntime as ort
+import torch
+
+from optimum.onnxruntime.configuration import AutoQuantizationConfig, OptimizationConfig, ORTConfig
+from optimum.onnxruntime.utils import get_device_for_provider, get_provider_for_device
+
+
+class ProviderAndDeviceGettersTest(unittest.TestCase):
+    def test_get_device_for_provider(self):
+        self.assertEqual(get_device_for_provider("CPUExecutionProvider", provider_options={}), torch.device("cpu"))
+        self.assertEqual(
+            get_device_for_provider("CUDAExecutionProvider", provider_options={"device_id": 1}), torch.device("cuda:1")
+        )
+
+    def test_get_provider_for_device(self):
+        self.assertEqual(get_provider_for_device(torch.device("cpu")), "CPUExecutionProvider")
+
+        if "ROCMExecutionProvider" in ort.get_available_providers():
+            self.assertEqual(get_provider_for_device(torch.device("cuda")), "ROCMExecutionProvider")
+        else:
+            self.assertEqual(get_provider_for_device(torch.device("cuda")), "CUDAExecutionProvider")
+
+
+class ORTConfigTest(unittest.TestCase):
+    def test_save_and_load(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            quantization_config = AutoQuantizationConfig.arm64(is_static=False, per_channel=False)
+            optimization_config = OptimizationConfig(optimization_level=2)
+            ort_config = ORTConfig(opset=11, quantization=quantization_config, optimization=optimization_config)
+            ort_config.save_pretrained(tmp_dir)
+            loaded_ort_config = ORTConfig.from_pretrained(tmp_dir)
+            self.assertEqual(ort_config.to_dict(), loaded_ort_config.to_dict())