Update doc examples feature extractor -> image processor (#20501)

* Update doc example feature extractor -> image processor

* Apply suggestions from code review

docs/source/en/preprocessing.mdx

@@ -19,11 +19,11 @@ Before you can train a model on a dataset, it needs to be preprocessed into the
 * Text, use a [Tokenizer](./main_classes/tokenizer) to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
 * Image inputs use a [ImageProcessor](./main_classes/image) to convert images into tensors.
 * Speech and audio, use a [Feature extractor](./main_classes/feature_extractor) to extract sequential features from audio waveforms and convert them into tensors.
-* Multimodal inputs, use a [Processor](./main_classes/processors) to combine a tokenizer and a feature extractor.
+* Multimodal inputs, use a [Processor](./main_classes/processors) to combine a tokenizer and a feature extractor or image processor.
 
 <Tip>
 
-`AutoProcessor` **always** works and automatically chooses the correct class for the model you're using, whether you're using a tokenizer, feature extractor or processor.
+`AutoProcessor` **always** works and automatically chooses the correct class for the model you're using, whether you're using a tokenizer, image processor, feature extractor or processor.
 
 </Tip>
 
@@ -320,9 +320,9 @@ The sample lengths are now the same and match the specified maximum length. You
 
 ## Computer vision
 
-For computer vision tasks, you'll need a [feature extractor](main_classes/feature_extractor) to prepare your dataset for the model. The feature extractor is designed to extract features from images, and convert them into tensors.
+For computer vision tasks, you'll need an [image processor](main_classes/image_processor) to prepare your dataset for the model. The image processor is designed to preprocess images, and convert them into tensors.
 
-Load the [food101](https://huggingface.co/datasets/food101) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a feature extractor with computer vision datasets: 
+Load the [food101](https://huggingface.co/datasets/food101) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use an image processor with computer vision datasets:
 
 <Tip>
 
@@ -346,17 +346,17 @@ Next, take a look at the image with 🤗 Datasets [`Image`](https://huggingface.
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png"/>
 </div>
 
-Load the feature extractor with [`AutoFeatureExtractor.from_pretrained`]:
+Load the image processor with [`AutoImageProcessor.from_pretrained`]:
 
 ```py
->>> from transformers import AutoFeatureExtractor
+>>> from transformers import AutoImageProcessor
 
->>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
+>>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
 ```
 
 For computer vision tasks, it is common to add some type of data augmentation to the images as a part of preprocessing. You can add augmentations with any library you'd like, but in this tutorial, you'll use torchvision's [`transforms`](https://pytorch.org/vision/stable/transforms.html) module. If you're interested in using another data augmentation library, learn how in the [Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) or [Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb).
 
-1. Normalize the image with the feature extractor and use [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) to chain some transforms - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - together:
+1. Normalize the image with the image processor and use [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) to chain some transforms - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - together:
 
 ```py
 >>> from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ColorJitter, ToTensor
@@ -370,7 +370,7 @@ For computer vision tasks, it is common to add some type of data augmentation to
 >>> _transforms = Compose([RandomResizedCrop(size), ColorJitter(brightness=0.5, hue=0.5), ToTensor(), normalize])
 ```
 
-2. The model accepts [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) as its input, which is generated by the feature extractor. Create a function that generates `pixel_values` from the transforms:
+2. The model accepts [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) as its input, which is generated by the image processor. Create a function that generates `pixel_values` from the transforms:
 
 ```py
 >>> def transforms(examples):
@@ -384,7 +384,7 @@ For computer vision tasks, it is common to add some type of data augmentation to
 >>> dataset.set_transform(transforms)
 ```
 
-4. Now when you access the image, you'll notice the feature extractor has added `pixel_values`. You can pass your processed dataset to the model now!
+4. Now when you access the image, you'll notice the image processor has added `pixel_values`. You can pass your processed dataset to the model now!
 
 ```py
 >>> dataset[0]["image"]
@@ -431,7 +431,7 @@ Here is what the image looks like after the transforms are applied. The image ha
 
 ## Multimodal
 
-For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples a tokenizer and feature extractor.
+For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples together two processing objects such as as tokenizer and feature extractor.
 
 Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):