test with transformers pipeline

tests/onnxruntime/test_modeling.py

@@ -115,6 +115,7 @@
     require_hf_token,
     require_ort_rocm,
 )
+from transformers import pipeline as transformers_pipeline
 
 
 if is_diffusers_available():
@@ -1747,7 +1748,7 @@ def test_pipeline_ort_model(self, model_arch):
 
     @pytest.mark.run_in_series
     def test_pipeline_model_is_none(self):
-        pipe = pipeline("text-classification")
+        pipe = transformers_pipeline("text-classification")
         text = "My Name is Philipp and i live in Germany."
         outputs = pipe(text)
 
@@ -2553,7 +2554,7 @@ def test_pipeline_ort_model(self, test_name: str, model_arch: str, use_cache: bo
 
     @pytest.mark.run_in_series
     def test_pipeline_model_is_none(self):
-        pipe = pipeline("text-generation")
+        pipe = transformers_pipeline("text-generation")
         text = "My Name is Philipp and i live"
         outputs = pipe(text)
 
@@ -3944,20 +3945,20 @@ def test_pipeline_text_generation(self, test_name: str, model_arch: str, use_cac
     @pytest.mark.run_in_series
     def test_pipeline_model_is_none(self):
         # Text2text generation
-        pipe = pipeline("text2text-generation")
+        pipe = transformers_pipeline("text2text-generation")
         text = "This is a test"
         outputs = pipe(text, min_length=1, max_length=2)
         # compare model output class
         self.assertIsInstance(outputs[0]["generated_text"], str)
 
         # Summarization
-        pipe = pipeline("summarization")
+        pipe = transformers_pipeline("summarization")
         outputs = pipe(text, min_length=1, max_length=2)
         # compare model output class
         self.assertIsInstance(outputs[0]["summary_text"], str)
 
         # Translation
-        pipe = pipeline("translation_en_to_de")
+        pipe = transformers_pipeline("translation_en_to_de")
         outputs = pipe(text, min_length=1, max_length=2)
         # compare model output class
         self.assertIsInstance(outputs[0]["translation_text"], str)