Funnel transformer (#6908)

* Initial model

* Fix upsampling

* Add special cls token id and test

* Formatting

* Test and fist FunnelTokenizerFast

* Common tests

* Fix the check_repo script and document Funnel

* Doc fixes

* Add all models

* Write doc

* Fix test

* Initial model

* Fix upsampling

* Add special cls token id and test

* Formatting

* Test and fist FunnelTokenizerFast

* Common tests

* Fix the check_repo script and document Funnel

* Doc fixes

* Add all models

* Write doc

* Fix test

* Fix copyright

* Forgot some layers can be repeated

* Apply suggestions from code review

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update src/transformers/modeling_funnel.py

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

* Address review comments

* Update src/transformers/modeling_funnel.py

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Address review comments

* Update src/transformers/modeling_funnel.py

Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

* Slow integration test

* Make small integration test

* Formatting

* Add checkpoint and separate classification head

* Formatting

* Expand list, fix link and add in pretrained models

* Styling

* Add the model in all summaries

* Typo fixes

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>

docs/source/model_doc/funnel.rst

@@ -0,0 +1,126 @@
+Funnel Transformer
+------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~
+
+The Funnel Transformer model was proposed in the paper
+`Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
+<https://arxiv.org/abs/2006.03236>`__.
+It is a bidirectional transformer model, like BERT, but with a pooling operation after each block of layers, a bit
+like in traditional convolutional neural networks (CNN) in computer vision.
+
+The abstract from the paper is the following:
+
+*With the success of language pretraining, it is highly desirable to develop more efficient architectures of good
+scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the
+much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only
+require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which
+gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More
+importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further
+improve the model capacity. In addition, to perform token-level predictions as required by common pretraining
+objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence
+via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on
+a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading
+comprehension.*
+
+Tips:
+
+- Since Funnel Transformer uses pooling, the sequence length of the hidden states changes after each block of layers.
+  The base model therefore has a final sequence length that is a quarter of the original one. This model can be used
+  directly for tasks that just require a sentence summary (like sequence classification or multiple choice). For other
+  tasks, the full model is used; this full model has a decoder that upsamples the final hidden states to the same
+  sequence length as the input.
+- The Funnel Transformer checkpoints are all available with a full version and a base version. The first ones should
+  be used for :class:`~transformers.FunnelModel`, :class:`~transformers.FunnelForPreTraining`,
+  :class:`~transformers.FunnelForMaskedLM`, :class:`~transformers.FunnelForTokenClassification` and
+  class:`~transformers.FunnelForQuestionAnswering`. The second ones should be used for
+  :class:`~transformers.FunnelBaseModel`, :class:`~transformers.FunnelForSequenceClassification` and
+  :class:`~transformers.FunnelForMultipleChoice`.
+
+The original code can be found `here <https://github.com/laiguokun/Funnel-Transformer>`_.
+
+
+FunnelConfig
+~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelConfig
+    :members:
+
+
+FunnelTokenizer
+~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelTokenizer
+    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
+        create_token_type_ids_from_sequences, save_vocabulary
+
+
+FunnelTokenizerFast
+~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelTokenizerFast
+    :members:
+
+
+Funnel specific outputs
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.modeling_funnel.FunnelForPreTrainingOutput
+    :members:
+
+
+FunnelBaseModel
+~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelBaseModel
+    :members:
+
+
+FunnelModel
+~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelModel
+    :members:
+
+
+FunnelModelForPreTraining
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForPreTraining
+    :members:
+
+
+FunnelForMaskedLM
+~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForMaskedLM
+    :members:
+
+
+FunnelForSequenceClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForSequenceClassification
+    :members:
+
+
+FunnelForMultipleChoice
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForMultipleChoice
+    :members:
+
+
+FunnelForTokenClassification
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForTokenClassification
+    :members:
+
+
+FunnelForQuestionAnswering
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.FunnelForQuestionAnswering
+    :members: