Allow FP16 or other precision inference for Pipelines (#31342)

* cast image features to model.dtype where needed to support FP16 or other precision in pipelines

* Update src/transformers/pipelines/image_feature_extraction.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Use .to instead

* Add FP16 pipeline support for zeroshot audio classification

* Remove unused torch imports

* Add docs on FP16 pipeline

* Remove unused import

* Add FP16 tests to pipeline mixin

* Add fp16 placeholder for mask_generation pipeline test

* Add FP16 tests for all pipelines

* Fix formatting

* Remove torch_dtype arg from is_pipeline_test_to_skip*

* Fix format

* trigger ci

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

tests/pipelines/test_pipelines_video_classification.py

@@ -38,11 +38,13 @@ from .test_pipelines_common import ANY
 class VideoClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
 
-    def get_test_pipeline(self, model, tokenizer, processor):
+    def get_test_pipeline(self, model, tokenizer, processor, torch_dtype="float32"):
         example_video_filepath = hf_hub_download(
             repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset"
         )
-        video_classifier = VideoClassificationPipeline(model=model, image_processor=processor, top_k=2)
+        video_classifier = VideoClassificationPipeline(
+            model=model, image_processor=processor, top_k=2, torch_dtype=torch_dtype
+        )
         examples = [
             example_video_filepath,
             "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",