Map model_type and doc pages names (#14944)

* Map model_type and doc pages names

* Add script

* Fix typo

* Quality

* Manual check for Auto

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

docs/source/model_doc/openai-gpt.mdx

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+<!--Copyright 2020 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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+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# OpenAI GPT
+
+## Overview
+
+OpenAI GPT model was proposed in [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
+by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional) transformer
+pre-trained using language modeling on a large corpus will long range dependencies, the Toronto Book Corpus.
+
+The abstract from the paper is the following:
+
+*Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering,
+semantic similarity assessment, and document classification. Although large unlabeled text corpora are abundant,
+labeled data for learning these specific tasks is scarce, making it challenging for discriminatively trained models to
+perform adequately. We demonstrate that large gains on these tasks can be realized by generative pretraining of a
+language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task. In
+contrast to previous approaches, we make use of task-aware input transformations during fine-tuning to achieve
+effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our
+approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms
+discriminatively trained models that use architectures specifically crafted for each task, significantly improving upon
+the state of the art in 9 out of the 12 tasks studied.*
+
+Tips:
+
+- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
+  the left.
+- GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
+  token in a sequence. Leveraging this feature allows GPT-2 to generate syntactically coherent text as it can be
+  observed in the *run_generation.py* example script.
+
+[Write With Transformer](https://transformer.huggingface.co/doc/gpt) is a webapp created and hosted by Hugging Face
+showcasing the generative capabilities of several models. GPT is one of them.
+
+This model was contributed by [thomwolf](https://huggingface.co/thomwolf). The original code can be found [here](https://github.com/openai/finetune-transformer-lm).
+
+Note:
+
+If you want to reproduce the original tokenization process of the *OpenAI GPT* paper, you will need to install `ftfy`
+and `SpaCy`:
+
+```bash
+pip install spacy ftfy==4.4.3
+python -m spacy download en
+```
+
+If you don't install `ftfy` and `SpaCy`, the [`OpenAIGPTTokenizer`] will default to tokenize
+using BERT's `BasicTokenizer` followed by Byte-Pair Encoding (which should be fine for most usage, don't worry).
+
+## OpenAIGPTConfig
+
+[[autodoc]] OpenAIGPTConfig
+
+## OpenAIGPTTokenizer
+
+[[autodoc]] OpenAIGPTTokenizer
+    - save_vocabulary
+
+## OpenAIGPTTokenizerFast
+
+[[autodoc]] OpenAIGPTTokenizerFast
+
+## OpenAI specific outputs
+
+[[autodoc]] models.openai.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
+
+[[autodoc]] models.openai.modeling_tf_openai.TFOpenAIGPTDoubleHeadsModelOutput
+
+## OpenAIGPTModel
+
+[[autodoc]] OpenAIGPTModel
+    - forward
+
+## OpenAIGPTLMHeadModel
+
+[[autodoc]] OpenAIGPTLMHeadModel
+    - forward
+
+## OpenAIGPTDoubleHeadsModel
+
+[[autodoc]] OpenAIGPTDoubleHeadsModel
+    - forward
+
+## OpenAIGPTForSequenceClassification
+
+[[autodoc]] OpenAIGPTForSequenceClassification
+    - forward
+
+## TFOpenAIGPTModel
+
+[[autodoc]] TFOpenAIGPTModel
+    - call
+
+## TFOpenAIGPTLMHeadModel
+
+[[autodoc]] TFOpenAIGPTLMHeadModel
+    - call
+
+## TFOpenAIGPTDoubleHeadsModel
+
+[[autodoc]] TFOpenAIGPTDoubleHeadsModel
+    - call
+
+## TFOpenAIGPTForSequenceClassification
+
+[[autodoc]] TFOpenAIGPTForSequenceClassification
+    - call