Examples reorg (#11350)

* Base move

* Examples reorganization

* Update references

* Put back test data

* Move conftest

* More fixes

* Move test data to test fixtures

* Update path

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* Address review comments and clean

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

docs/source/model_doc/bart.rst

@@ -43,7 +43,7 @@ Examples
 _______________________________________________________________________________________________________________________
 
 - Examples and scripts for fine-tuning BART and other models for sequence to sequence tasks can be found in
-  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
+  :prefix_link:`examples/pytorch/summarization/ <examples/pytorch/summarization/README.md>`.
 - An example of how to train :class:`~transformers.BartForConditionalGeneration` with a Hugging Face :obj:`datasets`
   object can be found in this `forum discussion
   <https://discuss.huggingface.co/t/train-bart-for-conditional-generation-e-g-summarization/1904>`__.

docs/source/model_doc/barthez.rst

@@ -43,7 +43,7 @@ Examples
 _______________________________________________________________________________________________________________________
 
 - BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check:
-  :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
+  :prefix_link:`examples/pytorch/summarization/ <examples/pytorch/summarization/README.md>`.
 
 
 BarthezTokenizer

docs/source/model_doc/distilbert.rst

@@ -44,8 +44,8 @@ Tips:
 - DistilBERT doesn't have options to select the input positions (:obj:`position_ids` input). This could be added if
   necessary though, just let us know if you need this option.
 
-This model was contributed by `victorsanh <https://huggingface.co/victorsanh>`__. The original code can be found `here
-<https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
+This model was contributed by `victorsanh <https://huggingface.co/victorsanh>`__. The original code can be found
+:prefix_link:`here <examples/research-projects/distillation>`.
 
 
 DistilBertConfig

docs/source/model_doc/pegasus.rst

@@ -53,7 +53,8 @@ Examples
 _______________________________________________________________________________________________________________________
 
 - :prefix_link:`Script <examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh>` to fine-tune pegasus
-  on the XSUM dataset. Data download instructions at :prefix_link:`examples/seq2seq/ <examples/seq2seq/README.md>`.
+  on the XSUM dataset. Data download instructions at :prefix_link:`examples/pytorch/summarization/
+  <examples/pytorch/summarization/README.md>`.
 - FP16 is not supported (help/ideas on this appreciated!).
 - The adafactor optimizer is recommended for pegasus fine-tuning.
 

docs/source/model_doc/retribert.rst

@@ -21,7 +21,7 @@ Question Answering <https://yjernite.github.io/lfqa.html>`__. RetriBERT is a sma
 pair of BERT encoders with lower-dimension projection for dense semantic indexing of text.
 
 This model was contributed by `yjernite <https://huggingface.co/yjernite>`__. Code to train and use the model can be
-found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__.
+found :prefix_link:`here <examples/research-projects/distillation>`.
 
 
 RetriBertConfig

docs/source/model_doc/xlnet.rst

@@ -41,7 +41,7 @@ Tips:
   using only a sub-set of the output tokens as target which are selected with the :obj:`target_mapping` input.
 - To use XLNet for sequential decoding (i.e. not in fully bi-directional setting), use the :obj:`perm_mask` and
   :obj:`target_mapping` inputs to control the attention span and outputs (see examples in
-  `examples/text-generation/run_generation.py`)
+  `examples/pytorch/text-generation/run_generation.py`)
 - XLNet is one of the few models that has no sequence length limit.
 
 This model was contributed by `thomwolf <https://huggingface.co/thomwolf>`__. The original code can be found `here