Migrate doc files to Markdown. (#24376)

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* Address review comment

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* Update utils/tests_fetcher.py

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/nllb.md

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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
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+
+# NLLB
+
+**DISCLAIMER:** The default behaviour for the tokenizer has recently been fixed (and thus changed)!
+
+The previous version adds `[self.eos_token_id, self.cur_lang_code]` at the end of the token sequence for both target and source tokenization. This is wrong as the NLLB paper mentions (page 48, 6.1.1. Model Architecture) :
+
+*Note that we prefix the source sequence with the source language, as opposed to the target
+language as previously done in several works (Arivazhagan et al., 2019; Johnson et al.,
+2017). This is primarily because we prioritize optimizing zero-shot performance of our
+model on any pair of 200 languages at a minor cost to supervised performance.*
+
+Previous behaviour:
+
+```python
+>>> from transformers import NllbTokenizer
+
+>>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+>>> tokenizer("How was your day?").input_ids
+[13374, 1398, 4260, 4039, 248130, 2, 256047]
+
+>>> # 2: '</s>'
+>>> # 256047 : 'eng_Latn'
+```
+New behaviour
+
+```python
+>>> from transformers import NllbTokenizer
+
+>>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+>>> tokenizer("How was your day?").input_ids
+[256047, 13374, 1398, 4260, 4039, 248130, 2]
+ ```
+
+Enabling the old behaviour can be done as follows:
+```python
+>>> from transformers import NllbTokenizer
+
+>>> tokenizer = NllbTokenizer.from_pretrained("facebook/nllb-200-distilled-600M", legacy_behaviour=True)
+```
+
+For more details, feel free to check the linked [PR](https://github.com/huggingface/transformers/pull/22313) and [Issue](https://github.com/huggingface/transformers/issues/19943).
+
+## Overview of NLLB
+
+The NLLB model was presented in [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by Marta R. Costa-jussà, James Cross, Onur Çelebi,
+Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula,
+Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit, Chau Tran, Pierre Andrews,
+Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe Ropers,
+Safiyyah Saleem, Holger Schwenk, and Jeff Wang.
+
+The abstract of the paper is the following:
+
+*Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
+However, such efforts have coalesced around a small subset of languages, leaving behind the vast majority of mostly low-resource languages. What does it take to break the
+200 language barrier while ensuring safe, high quality results, all while keeping ethical considerations in mind? In No Language Left Behind, we took on this challenge by
+first contextualizing the need for low-resource language translation support through exploratory interviews with native speakers. Then, we created datasets and models aimed
+at narrowing the performance gap between low and high-resource languages. More specifically, we developed a conditional compute model based on Sparsely Gated Mixture of
+Experts that is trained on data obtained with novel and effective data mining techniques tailored for low-resource languages. We propose multiple architectural and training
+improvements to counteract overfitting while training on thousands of tasks. Critically, we evaluated the performance of over 40,000 different translation directions using
+a human-translated benchmark, Flores-200, and combined human evaluation with a novel toxicity benchmark covering all languages in Flores-200 to assess translation safety.
+Our model achieves an improvement of 44% BLEU relative to the previous state-of-the-art, laying important groundwork towards realizing a universal translation system.*
+
+This implementation contains the dense models available on release.
+
+**The sparse model NLLB-MoE (Mixture of Expert) is now available! More details [here](nllb-moe)**
+
+This model was contributed by [Lysandre](https://huggingface.co/lysandre). The authors' code can be found [here](https://github.com/facebookresearch/fairseq/tree/nllb).
+
+## Generating with NLLB
+
+While generating the target text set the `forced_bos_token_id` to the target language id. The following
+example shows how to translate English to French using the *facebook/nllb-200-distilled-600M* model.
+
+Note that we're using the BCP-47 code for French `fra_Latn`. See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
+for the list of all BCP-47 in the Flores 200 dataset.
+
+```python
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+
+>>> article = "UN Chief says there is no military solution in Syria"
+>>> inputs = tokenizer(article, return_tensors="pt")
+
+>>> translated_tokens = model.generate(
+...     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["fra_Latn"], max_length=30
+... )
+>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+Le chef de l'ONU dit qu'il n'y a pas de solution militaire en Syrie
+```
+
+### Generating from any other language than English
+
+English (`eng_Latn`) is set as the default language from which to translate. In order to specify that you'd like to translate from a different language,
+you should specify the BCP-47 code in the `src_lang` keyword argument of the tokenizer initialization.
+
+See example below for a translation from romanian to german:
+
+```py
+>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained(
+...     "facebook/nllb-200-distilled-600M", use_auth_token=True, src_lang="ron_Latn"
+... )
+>>> model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", use_auth_token=True)
+
+>>> article = "Şeful ONU spune că nu există o soluţie militară în Siria"
+>>> inputs = tokenizer(article, return_tensors="pt")
+
+>>> translated_tokens = model.generate(
+...     **inputs, forced_bos_token_id=tokenizer.lang_code_to_id["deu_Latn"], max_length=30
+... )
+>>> tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
+UN-Chef sagt, es gibt keine militärische Lösung in Syrien
+```
+
+## Documentation resources
+
+- [Translation task guide](../tasks/translation)
+- [Summarization task guide](../tasks/summarization)
+
+## NllbTokenizer
+
+[[autodoc]] NllbTokenizer
+    - build_inputs_with_special_tokens
+
+## NllbTokenizerFast
+
+[[autodoc]] NllbTokenizerFast