Migrate doc files to Markdown. (#24376)

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

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Co-authored-by: Lysandre Debut <lysandre.debut@reseau.eseo.fr>

docs/source/en/model_doc/sam.md

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+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# SAM
+
+## Overview
+
+SAM (Segment Anything Model) was proposed in [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
+
+The model can be used to predict segmentation masks of any object of interest given an input image. 
+
+![example image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/sam-output.png)
+
+The abstract from the paper is the following:
+
+*We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensed and privacy respecting images. The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks. We evaluate its capabilities on numerous tasks and find that its zero-shot performance is impressive -- often competitive with or even superior to prior fully supervised results. We are releasing the Segment Anything Model (SAM) and corresponding dataset (SA-1B) of 1B masks and 11M images at [https://segment-anything.com](https://segment-anything.com) to foster research into foundation models for computer vision.*
+
+Tips:
+
+- The model predicts binary masks that states the presence or not of the object of interest given an image.
+- The model predicts much better results if input 2D points and/or input bounding boxes are provided
+- You can prompt multiple points for the same image, and predict a single mask. 
+- Fine-tuning the model is not supported yet
+- According to the paper, textual input should be also supported. However, at this time of writing this seems to be not supported according to [the official repository](https://github.com/facebookresearch/segment-anything/issues/4#issuecomment-1497626844). 
+
+
+This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
+The original code can be found [here](https://github.com/facebookresearch/segment-anything).
+
+Below is an example on how to run mask generation given an image and a 2D point:
+
+```python
+import torch
+from PIL import Image
+import requests
+from transformers import SamModel, SamProcessor
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
+processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")
+
+img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
+raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
+input_points = [[[450, 600]]]  # 2D location of a window in the image
+
+inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
+outputs = model(**inputs)
+
+masks = processor.image_processor.post_process_masks(
+    outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
+)
+scores = outputs.iou_scores
+```
+
+Resources:
+
+- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/segment_anything.ipynb) for using the model.
+- [Demo notebook](https://github.com/huggingface/notebooks/blob/main/examples/automatic_mask_generation.ipynb) for using the automatic mask generation pipeline.
+- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Run_inference_with_MedSAM_using_HuggingFace_Transformers.ipynb) for inference with MedSAM, a fine-tuned version of SAM on the medical domain.
+- [Demo notebook](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/SAM/Fine_tune_SAM_(segment_anything)_on_a_custom_dataset.ipynb) for fine-tuning the model on custom data.
+
+## SamConfig
+
+[[autodoc]] SamConfig
+
+## SamVisionConfig
+
+[[autodoc]] SamVisionConfig
+
+## SamMaskDecoderConfig
+
+[[autodoc]] SamMaskDecoderConfig
+
+## SamPromptEncoderConfig
+
+[[autodoc]] SamPromptEncoderConfig
+
+
+## SamProcessor
+
+[[autodoc]] SamProcessor
+
+
+## SamImageProcessor
+
+[[autodoc]] SamImageProcessor
+
+
+## SamModel
+
+[[autodoc]] SamModel
+    - forward
+
+
+## TFSamModel
+
+[[autodoc]] TFSamModel
+    - call