AutoImageProcessor (#20111)

* AutoImageProcessor skeleton

* Update references

* Add mapping in init

* Add model image processors to __init__ for importing

* Add AutoImageProcessor tests

* Fix up

* Image Processor documentation

* Remove pdb

* Update docs/source/en/model_doc/mobilevit.mdx

* Update docs

* Don't add whitespace on json files

* Remove fixtures

* Move checking model config down

* Fix up

* Add check for image processor

* Remove FeatureExtractorMixin in docstrings

* Rename model_tmpfile to config_tmpfile

* Don't make None if not in image processor map

docs/source/en/model_doc/flava.mdx

@@ -16,17 +16,17 @@ specific language governing permissions and limitations under the License.
 
 The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
 
-The paper aims at creating a single unified foundation model which can work across vision, language 
+The paper aims at creating a single unified foundation model which can work across vision, language
 as well as vision-and-language multimodal tasks.
 
 The abstract from the paper is the following:
 
-*State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety 
-of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal 
-(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising 
-direction would be to use a single holistic universal model, as a "foundation", that targets all modalities 
-at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and 
-cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate 
+*State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety
+of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal
+(with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising
+direction would be to use a single holistic universal model, as a "foundation", that targets all modalities
+at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and
+cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate
 impressive performance on a wide range of 35 tasks spanning these target modalities.*
 
 
@@ -61,6 +61,11 @@ This model was contributed by [aps](https://huggingface.co/aps). The original co
 
 [[autodoc]] FlavaFeatureExtractor
 
+## FlavaImageProcessor
+
+[[autodoc]] FlavaImageProcessor
+    - preprocess
+
 ## FlavaForPreTraining
 
 [[autodoc]] FlavaForPreTraining