Deprecate low use models (#30781)

* Deprecate models
- graphormer
- time_series_transformer
- xlm_prophetnet
- qdqbert
- nat
- ernie_m
- tvlt
- nezha
- mega
- jukebox
- vit_hybrid
- x_clip
- deta
- speech_to_text_2
- efficientformer
- realm
- gptsan_japanese

* Fix up

* Fix speech2text2 imports

* Make sure message isn't indented

* Fix docstrings

* Correctly map for deprecated models from model_type

* Uncomment out

* Add back time series transformer and x-clip

* Import fix and fix-up

* Fix up with updated ruff

docs/source/en/model_doc/efficientformer.md

@@ -16,28 +16,36 @@ rendered properly in your Markdown viewer.
 
 # EfficientFormer
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, we don't accept any new PRs changing its code.
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.40.2.
+You can do so by running the following command: `pip install -U transformers==4.40.2`.
+
+</Tip>
+
 ## Overview
 
-The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191) 
+The EfficientFormer model was proposed in [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191)
 by Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.  EfficientFormer proposes a
 dimension-consistent pure transformer that can be run on mobile devices for dense prediction tasks like image classification, object
 detection and semantic segmentation.
 
 The abstract from the paper is the following:
 
-*Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. 
-However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally 
-times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly 
-challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation 
-complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still 
-unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? 
-To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. 
-Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm. 
-Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. 
-Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. 
-Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on 
-iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model, 
-EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can 
+*Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks.
+However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally
+times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly
+challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation
+complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still
+unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance?
+To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs.
+Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm.
+Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer.
+Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices.
+Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on
+iPhone 12 (compiled with CoreML), which { runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1),} and our largest model,
+EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can
 reach extremely low latency on mobile devices while maintaining high performance.*
 
 This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd).
@@ -93,4 +101,4 @@ The original code can be found [here](https://github.com/snap-research/Efficient
     - call
 
 </tf>
-</frameworkcontent>
+</frameworkcontent>