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_document_question_answering.py

@@ -61,9 +61,13 @@ class DocumentQuestionAnsweringPipelineTests(unittest.TestCase):
 
     @require_pytesseract
     @require_vision
-    def get_test_pipeline(self, model, tokenizer, processor):
+    def get_test_pipeline(self, model, tokenizer, processor, torch_dtype="float32"):
         dqa_pipeline = pipeline(
-            "document-question-answering", model=model, tokenizer=tokenizer, image_processor=processor
+            "document-question-answering",
+            model=model,
+            tokenizer=tokenizer,
+            image_processor=processor,
+            torch_dtype=torch_dtype,
         )
 
         image = INVOICE_URL