Decorators for deprecation and named arguments validation (#30799)

* Fix do_reduce_labels for maskformer image processor

* Deprecate reduce_labels in favor to do_reduce_labels

* Deprecate reduce_labels in favor to do_reduce_labels (segformer)

* Deprecate reduce_labels in favor to do_reduce_labels (oneformer)

* Deprecate reduce_labels in favor to do_reduce_labels (maskformer)

* Deprecate reduce_labels in favor to do_reduce_labels (mask2former)

* Fix typo

* Update mask2former test

* fixup

* Update segmentation examples

* Update docs

* Fixup

* Imports fixup

* Add deprecation decorator draft

* Add deprecation decorator

* Fixup

* Add deprecate_kwarg decorator

* Validate kwargs decorator

* Kwargs validation (beit)

* fixup

* Kwargs validation (mask2former)

* Kwargs validation (maskformer)

* Kwargs validation (oneformer)

* Kwargs validation (segformer)

* Better message

* Fix oneformer processor save-load test

* Update src/transformers/utils/deprecation.py

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

* Update src/transformers/utils/deprecation.py

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

* Update src/transformers/utils/deprecation.py

Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>

* Update src/transformers/utils/deprecation.py

Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>

* Better handle classmethod warning

* Fix typo, remove warn

* Add header

* Docs and `additional_message`

* Move to filter decorator ot generic

* Proper deprecation for semantic segm scripts

* Add to __init__ and update import

* Basic tests for filter decorator

* Fix doc

* Override `to_dict()` to pop depracated `_max_size`

* Pop unused parameters

* Fix trailing whitespace

* Add test for deprecation

* Add deprecation warning control parameter

* Update generic test

* Fixup deprecation tests

* Introduce init service kwargs

* Revert popping unused params

* Revert oneformer test

* Allow "metadata" to pass

* Better docs

* Fix test

* Add notion in docstring

* Fix notification for both names

* Add func name to warning message

* Fixup

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Pablo Montalvo <39954772+molbap@users.noreply.github.com>

docs/source/en/model_doc/segformer.md

@@ -66,12 +66,12 @@ of the model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). T
   important preprocessing step is that images and segmentation maps are randomly cropped and padded to the same size,
   such as 512x512 or 640x640, after which they are normalized.
 - One additional thing to keep in mind is that one can initialize [`SegformerImageProcessor`] with
-  `reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
+  `do_reduce_labels` set to `True` or `False`. In some datasets (like ADE20k), the 0 index is used in the annotated
   segmentation maps for background. However, ADE20k doesn't include the "background" class in its 150 labels.
-  Therefore, `reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
+  Therefore, `do_reduce_labels` is used to reduce all labels by 1, and to make sure no loss is computed for the
   background class (i.e. it replaces 0 in the annotated maps by 255, which is the *ignore_index* of the loss function
   used by [`SegformerForSemanticSegmentation`]). However, other datasets use the 0 index as
-  background class and include this class as part of all labels. In that case, `reduce_labels` should be set to
+  background class and include this class as part of all labels. In that case, `do_reduce_labels` should be set to
   `False`, as loss should also be computed for the background class.
 - As most models, SegFormer comes in different sizes, the details of which can be found in the table below
   (taken from Table 7 of the [original paper](https://arxiv.org/abs/2105.15203)).

docs/source/en/tasks/semantic_segmentation.md

@@ -310,13 +310,13 @@ As an example, take a look at this [example dataset](https://huggingface.co/data
 
 ### Preprocess
 
-The next step is to load a SegFormer image processor to prepare the images and annotations for the model. Some datasets, like this one, use the zero-index as the background class. However, the background class isn't actually included in the 150 classes, so you'll need to set `reduce_labels=True` to subtract one from all the labels. The zero-index is replaced by `255` so it's ignored by SegFormer's loss function:
+The next step is to load a SegFormer image processor to prepare the images and annotations for the model. Some datasets, like this one, use the zero-index as the background class. However, the background class isn't actually included in the 150 classes, so you'll need to set `do_reduce_labels=True` to subtract one from all the labels. The zero-index is replaced by `255` so it's ignored by SegFormer's loss function:
 
 ```py
 >>> from transformers import AutoImageProcessor
 
 >>> checkpoint = "nvidia/mit-b0"
->>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, do_reduce_labels=True)
 ```
 
 <frameworkcontent>

docs/source/ja/tasks/semantic_segmentation.md

@@ -96,13 +96,13 @@ pip install -q datasets transformers evaluate
 
 ## Preprocess
 
-次のステップでは、SegFormer 画像プロセッサをロードして、モデルの画像と注釈を準備します。このデータセットのような一部のデータセットは、バックグラウンド クラスとしてゼロインデックスを使用します。ただし、実際には背景クラスは 150 個のクラスに含まれていないため、`reduce_labels=True`を設定してすべてのラベルから 1 つを引く必要があります。ゼロインデックスは `255` に置き換えられるため、SegFormer の損失関数によって無視されます。
+次のステップでは、SegFormer 画像プロセッサをロードして、モデルの画像と注釈を準備します。このデータセットのような一部のデータセットは、バックグラウンド クラスとしてゼロインデックスを使用します。ただし、実際には背景クラスは 150 個のクラスに含まれていないため、`do_reduce_labels=True`を設定してすべてのラベルから 1 つを引く必要があります。ゼロインデックスは `255` に置き換えられるため、SegFormer の損失関数によって無視されます。
 
 ```py
 >>> from transformers import AutoImageProcessor
 
 >>> checkpoint = "nvidia/mit-b0"
->>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, do_reduce_labels=True)
 ```
 
 <frameworkcontent>

docs/source/ja/tasks/sequence_classification.md

@@ -96,13 +96,13 @@ pip install -q datasets transformers evaluate
 
 ## Preprocess
 
-次のステップでは、SegFormer 画像プロセッサをロードして、モデルの画像と注釈を準備します。このデータセットのような一部のデータセットは、バックグラウンド クラスとしてゼロインデックスを使用します。ただし、実際には背景クラスは 150 個のクラスに含まれていないため、`reduce_labels=True`を設定してすべてのラベルから 1 つを引く必要があります。ゼロインデックスは `255` に置き換えられるため、SegFormer の損失関数によって無視されます。
+次のステップでは、SegFormer 画像プロセッサをロードして、モデルの画像と注釈を準備します。このデータセットのような一部のデータセットは、バックグラウンド クラスとしてゼロインデックスを使用します。ただし、実際には背景クラスは 150 個のクラスに含まれていないため、`do_reduce_labels=True`を設定してすべてのラベルから 1 つを引く必要があります。ゼロインデックスは `255` に置き換えられるため、SegFormer の損失関数によって無視されます。
 
 ```py
 >>> from transformers import AutoImageProcessor
 
 >>> checkpoint = "nvidia/mit-b0"
->>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, do_reduce_labels=True)
 ```
 
 <frameworkcontent>

docs/source/ko/tasks/semantic_segmentation.md

@@ -95,13 +95,13 @@ pip install -q datasets transformers evaluate
 
 ## 전처리하기[[preprocess]
 
-다음 단계는 모델에 사용할 이미지와 주석을 준비하기 위해 SegFormer 이미지 프로세서를 불러오는 것입니다. 우리가 사용하는 데이터 세트와 같은 일부 데이터 세트는 배경 클래스로 제로 인덱스를 사용합니다. 하지만 배경 클래스는 150개의 클래스에 실제로는 포함되지 않기 때문에 `reduce_labels=True` 를 설정해 모든 레이블에서 배경 클래스를 제거해야 합니다. 제로 인덱스는 `255`로 대체되므로 SegFormer의 손실 함수에서 무시됩니다:
+다음 단계는 모델에 사용할 이미지와 주석을 준비하기 위해 SegFormer 이미지 프로세서를 불러오는 것입니다. 우리가 사용하는 데이터 세트와 같은 일부 데이터 세트는 배경 클래스로 제로 인덱스를 사용합니다. 하지만 배경 클래스는 150개의 클래스에 실제로는 포함되지 않기 때문에 `do_reduce_labels=True` 를 설정해 모든 레이블에서 배경 클래스를 제거해야 합니다. 제로 인덱스는 `255`로 대체되므로 SegFormer의 손실 함수에서 무시됩니다:
 
 ```py
 >>> from transformers import AutoImageProcessor
 
 >>> checkpoint = "nvidia/mit-b0"
->>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint, do_reduce_labels=True)
 ```
 
 <frameworkcontent>