Make pipeline able to load processor (#32514)

* Refactor get_test_pipeline

* Fixup

* Fixing tests

* Add processor loading in tests

* Restructure processors loading

* Add processor to the pipeline

* Move model loading on tom of the test

* Update `get_test_pipeline`

* Fixup

* Add class-based flags for loading processors

* Change `is_pipeline_test_to_skip` signature

* Skip t5 failing test for slow tokenizer

* Fixup

* Fix copies for T5

* Fix typo

* Add try/except for tokenizer loading (kosmos-2 case)

* Fixup

* Llama not fails for long generation

* Revert processor pass in text-generation test

* Fix docs

* Switch back to json file for image processors and feature extractors

* Add processor type check

* Remove except for tokenizers

* Fix docstring

* Fix empty lists for tests

* Fixup

* Fix load check

* Ensure we have non-empty test cases

* Update src/transformers/pipelines/__init__.py

Co-authored-by: Lysandre Debut <hi@lysand.re>

* Update src/transformers/pipelines/base.py

Co-authored-by: Lysandre Debut <hi@lysand.re>

* Rework comment

* Better docs, add note about pipeline components

* Change warning to error raise

* Fixup

* Refine pipeline docs

---------

Co-authored-by: Lysandre Debut <hi@lysand.re>

tests/pipelines/test_pipelines_feature_extraction.py

@@ -174,7 +174,15 @@ class FeatureExtractionPipelineTests(unittest.TestCase):
             raise TypeError("We expect lists of floats, nothing else")
         return shape
 
-    def get_test_pipeline(self, model, tokenizer, processor, torch_dtype="float32"):
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        torch_dtype="float32",
+    ):
         if tokenizer is None:
             self.skipTest(reason="No tokenizer")
         elif (
@@ -193,10 +201,15 @@ class FeatureExtractionPipelineTests(unittest.TestCase):
                 For now ignore those.
                 """
             )
-        feature_extractor = FeatureExtractionPipeline(
-            model=model, tokenizer=tokenizer, feature_extractor=processor, torch_dtype=torch_dtype
+        feature_extractor_pipeline = FeatureExtractionPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            torch_dtype=torch_dtype,
         )
-        return feature_extractor, ["This is a test", "This is another test"]
+        return feature_extractor_pipeline, ["This is a test", "This is another test"]
 
     def run_pipeline_test(self, feature_extractor, examples):
         outputs = feature_extractor("This is a test")