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/mobilevit.mdx

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 ## Overview
 
-The MobileViT model was proposed in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers. 
+The MobileViT model was proposed in [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari. MobileViT introduces a new layer that replaces local processing in convolutions with global processing using transformers.
 
 The abstract from the paper is the following:
 
@@ -25,10 +25,10 @@ Tips:
 - MobileViT is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map. You can follow [this tutorial](https://keras.io/examples/vision/mobilevit) for a lightweight introduction.
 - One can use [`MobileViTFeatureExtractor`] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
 - The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
-- The segmentation model uses a [DeepLabV3](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/). 
-- As the name suggests MobileViT was designed to be performant and efficient on mobile phones. The TensorFlow versions of the MobileViT models are fully compatible with [TensorFlow Lite](https://www.tensorflow.org/lite). 
+- The segmentation model uses a [DeepLabV3](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
+- As the name suggests MobileViT was designed to be performant and efficient on mobile phones. The TensorFlow versions of the MobileViT models are fully compatible with [TensorFlow Lite](https://www.tensorflow.org/lite).
 
-  You can use the following code to convert a MobileViT checkpoint (be it image classification or semantic segmentation) to generate a 
+  You can use the following code to convert a MobileViT checkpoint (be it image classification or semantic segmentation) to generate a
   TensorFlow Lite model:
 
 ```py
@@ -52,7 +52,7 @@ with open(tflite_filename, "wb") as f:
 ```
 
   The resulting model will be just **about an MB** making it a good fit for mobile applications where resources and network
-  bandwidth can be constrained. 
+  bandwidth can be constrained.
 
 
 This model was contributed by [matthijs](https://huggingface.co/Matthijs). The TensorFlow version of the model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). The original code and weights can be found [here](https://github.com/apple/ml-cvnets).
@@ -68,6 +68,12 @@ This model was contributed by [matthijs](https://huggingface.co/Matthijs). The T
     - __call__
     - post_process_semantic_segmentation
 
+## MobileViTImageProcessor
+
+[[autodoc]] MobileViTImageProcessor
+    - preprocess
+    - post_process_semantic_segmentation
+
 ## MobileViTModel
 
 [[autodoc]] MobileViTModel
@@ -86,14 +92,14 @@ This model was contributed by [matthijs](https://huggingface.co/Matthijs). The T
 ## TFMobileViTModel
 
 [[autodoc]] TFMobileViTModel
-    - call 
+    - call
 
 ## TFMobileViTForImageClassification
 
 [[autodoc]] TFMobileViTForImageClassification
-    - call 
+    - call
 
 ## TFMobileViTForSemanticSegmentation
 
 [[autodoc]] TFMobileViTForSemanticSegmentation
-    - call 
+    - call