mbart - marian - mobilebert - openai

longformer - lxmert
herbert and layoutlm
2020-11-25 23:52:09 +01:00 · 2020-11-25 19:30:33 +01:00 · 2020-11-25 19:14:55 +01:00 · 2020-11-25 19:03:07 +01:00 · 2020-11-25 18:49:18 +01:00 · 2020-11-25 18:36:19 +01:00
302 changed files with 4711 additions and 13715 deletions
@@ -3,22 +3,6 @@ orbs:
    gcp-gke: circleci/gcp-gke@1.0.4
    go: circleci/go@1.3.0

-commands:
-  skip-job-on-doc-only-changes:
-    description: "Do not continue this job and exit with success for PRs with only doc changes"
-    steps:
-
-      - run:
-          name: docs-only changes skip check
-          command: |
-            if git diff --name-only << pipeline.git.base_revision >>...<< pipeline.git.revision >> | egrep -qv '\.(md|rst)$'
-            then
-                echo "Non-docs were modified in this PR, proceeding normally"
-            else
-                echo "Only docs were modified in this PR, quitting this job"
-                circleci step halt
-            fi
-
 # TPU REFERENCES
 references:
    checkout_ml_testing: &checkout_ml_testing
@@ -88,7 +72,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch_and_tf-{{ checksum "setup.py" }}
@@ -115,7 +98,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch-{{ checksum "setup.py" }}
@@ -142,7 +124,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
@@ -169,7 +150,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                keys:
                    - v0.4-flax-{{ checksum "setup.py" }}
@@ -196,7 +176,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch-{{ checksum "setup.py" }}
@@ -223,7 +202,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-tf-{{ checksum "setup.py" }}
@@ -248,7 +226,6 @@ jobs:
            RUN_CUSTOM_TOKENIZERS: yes
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-custom_tokenizers-{{ checksum "setup.py" }}
@@ -276,7 +253,6 @@ jobs:
        parallelism: 1
        steps:
            - checkout
-            - skip-job-on-doc-only-changes
            - restore_cache:
                  keys:
                      - v0.4-torch_examples-{{ checksum "setup.py" }}
@@ -58,5 +58,5 @@ members/contributors which may be interested in your PR.
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
- FSMT: @stas00
+ FSTM: @stas00
 -->
@@ -57,13 +57,13 @@ jobs:
          python -c "import torch; print('Cuda available:', torch.cuda.is_available())"
          python -c "import torch; print('Number of GPUs available:', torch.cuda.device_count())"

-#      - name: Create model files
-#        run: |
-#          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+      - name: Create model files
+        run: |
+          source .env/bin/activate
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model

      - name: Run all non-slow tests on GPU
        env:
@@ -129,10 +129,10 @@ jobs:
      - name: Create model files
        run: |
          source .env/bin/activate
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
-#          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/pt-encoder-bert-tokenizer.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/standalone.json --path=templates/adding_a_new_model
+          transformers-cli add-new-model --testing --testing_file=templates/adding_a_new_model/tests/tf-encoder-bert-tokenizer.json --path=templates/adding_a_new_model

      - name: Run all non-slow tests on GPU
        env:
@@ -9,6 +9,7 @@ on:
  push:
    branches:
      - ci_*
+      - framework-agnostic-tokenizers
  repository_dispatch:
  schedule:
    - cron: "0 0 * * *"
@@ -125,7 +125,7 @@ Follow these steps to start contributing:
   $ git checkout -b a-descriptive-name-for-my-changes
   ```

-   **Do not** work on the `master` branch.
+   **do not** work on the `master` branch.

 4. Set up a development environment by running the following command in a virtual environment:

@@ -317,16 +317,3 @@ One way one can run the make command on Window is to pass by MSYS2:
 1. [Download MSYS2](https://www.msys2.org/), we assume to have it installed in C:\msys64
 2. Open the command line C:\msys64\msys2.exe (it should be available from the start menu)
 3. Run in the shell: `pacman -Syu` and install make with `pacman -S make`
-
-### Syncing forked master with upstream (HuggingFace) master
-
-To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnessary notifications to the developers involved in these PRs, 
-when syncing the master branch of a forked repository, please, follow these steps:
-1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked master.
-2. If a PR is absolutely necessary, use the following steps after checking out your branch:
-```
-$ git checkout -b your-branch-for-syncing
-$ git pull --squash --no-commit upstream master
-$ git commit -m '<your message without GitHub references>'
-$ git push --set-upstream origin your-branch-for-syncing
-```
@@ -1,4 +1,4 @@
-.PHONY: deps_table_update modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs
+.PHONY: modified_only_fixup extra_quality_checks quality style fixup fix-copies test test-examples docs


 check_dirs := examples tests src utils
@@ -14,14 +14,9 @@ modified_only_fixup:
 		echo "No library .py files were modified"; \
 	fi

-# Update src/transformers/dependency_versions_table.py
-
-deps_table_update:
-	@python setup.py deps_table_update
-
 # Check that source code meets quality standards

-extra_quality_checks: deps_table_update
+extra_quality_checks:
 	python utils/check_copies.py
 	python utils/check_dummies.py
 	python utils/check_repo.py
@@ -37,7 +32,7 @@ quality:

 # Format source code automatically and check is there are any problems left that need manual fixing

-style: deps_table_update
+style:
 	black $(check_dirs)
 	isort $(check_dirs)
 	python utils/style_doc.py src/transformers docs/source --max_len 119
@@ -160,7 +160,6 @@ If you'd like to play with the examples, you must [install the library from sour

 1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
 1. **[BART](https://huggingface.co/transformers/model_doc/bart.html)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/transformers/model_doc/barthez.html)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
 1. **[BERT](https://huggingface.co/transformers/model_doc/bert.html)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
 1. **[BERT For Sequence Generation](https://huggingface.co/transformers/model_doc/bertgeneration.html)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[Blenderbot](https://huggingface.co/transformers/model_doc/blenderbot.html)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
@@ -2,15 +2,6 @@

 /* Colab dropdown */

-table.center-aligned-table td {
-    text-align: center;
-}
-
-table.center-aligned-table th {
-    text-align: center;
-    vertical-align: middle;
-}
-
 .colab-dropdown {
    position: relative;
    display: inline-block;
@@ -35,8 +35,6 @@ Choose the right framework for every part of a model's lifetime:
 - Move a single model between TF2.0/PyTorch frameworks at will
 - Seamlessly pick the right framework for training, evaluation, production

-Experimental support for Flax with a few models right now, expected to grow in the coming months.
-
 Contents
 -----------------------------------------------------------------------------------------------------------------------

@@ -54,8 +52,8 @@ The documentation is organized in five parts:
    - **MODELS** for the classes and functions related to each model implemented in the library.
    - **INTERNAL HELPERS** for the classes and functions we use internally.

-The library currently contains PyTorch, Tensorflow and Flax implementations, pretrained model weights, usage scripts
-and conversion utilities for the following models:
+The library currently contains PyTorch and Tensorflow implementations, pre-trained model weights, usage scripts and
+conversion utilities for the following models:

 ..
    This list is updated automatically from the README with `make fix-copies`. Do not update manually!
@@ -68,198 +66,106 @@ and conversion utilities for the following models:
   Pre-training for Natural Language Generation, Translation, and Comprehension
   <https://arxiv.org/pdf/1910.13461.pdf>`__ by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman
   Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-3. :doc:`BARThez <model_doc/barthez>` (from École polytechnique) released with the paper `BARThez: a Skilled Pretrained
-   French Sequence-to-Sequence Model <https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P.
-   Tixier, Michalis Vazirgiannis.
-4. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
+3. :doc:`BERT <model_doc/bert>` (from Google) released with the paper `BERT: Pre-training of Deep Bidirectional
   Transformers for Language Understanding <https://arxiv.org/abs/1810.04805>`__ by Jacob Devlin, Ming-Wei Chang,
   Kenton Lee and Kristina Toutanova.
-5. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
+4. :doc:`BERT For Sequence Generation <model_doc/bertgeneration>` (from Google) released with the paper `Leveraging
   Pre-trained Checkpoints for Sequence Generation Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi
   Narayan, Aliaksei Severyn.
-6. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
+5. :doc:`Blenderbot <model_doc/blenderbot>` (from Facebook) released with the paper `Recipes for building an
   open-domain chatbot <https://arxiv.org/abs/2004.13637>`__ by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary
   Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-7. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
+6. :doc:`CamemBERT <model_doc/camembert>` (from Inria/Facebook/Sorbonne) released with the paper `CamemBERT: a Tasty
   French Language Model <https://arxiv.org/abs/1911.03894>`__ by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz
   Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-8. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
+7. :doc:`CTRL <model_doc/ctrl>` (from Salesforce) released with the paper `CTRL: A Conditional Transformer Language
   Model for Controllable Generation <https://arxiv.org/abs/1909.05858>`__ by Nitish Shirish Keskar*, Bryan McCann*,
   Lav R. Varshney, Caiming Xiong and Richard Socher.
-9. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
+8. :doc:`DeBERTa <model_doc/deberta>` (from Microsoft Research) released with the paper `DeBERTa: Decoding-enhanced
   BERT with Disentangled Attention <https://arxiv.org/abs/2006.03654>`__ by Pengcheng He, Xiaodong Liu, Jianfeng Gao,
   Weizhu Chen.
-10. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
-    Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe
-    Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-11. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
+9. :doc:`DialoGPT <model_doc/dialogpt>` (from Microsoft Research) released with the paper `DialoGPT: Large-Scale
+   Generative Pre-training for Conversational Response Generation <https://arxiv.org/abs/1911.00536>`__ by Yizhe Zhang,
+   Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
+10. :doc:`DistilBERT <model_doc/distilbert>` (from HuggingFace), released together with the paper `DistilBERT, a
    distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__ by Victor
    Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into `DistilGPT2
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, RoBERTa into `DistilRoBERTa
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__, Multilingual BERT into
    `DistilmBERT <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German
    version of DistilBERT.
-12. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
+11. :doc:`DPR <model_doc/dpr>` (from Facebook) released with the paper `Dense Passage Retrieval for Open-Domain
    Question Answering <https://arxiv.org/abs/2004.04906>`__ by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick
    Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-13. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
+12. :doc:`ELECTRA <model_doc/electra>` (from Google Research/Stanford University) released with the paper `ELECTRA:
    Pre-training text encoders as discriminators rather than generators <https://arxiv.org/abs/2003.10555>`__ by Kevin
    Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-14. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
+13. :doc:`FlauBERT <model_doc/flaubert>` (from CNRS) released with the paper `FlauBERT: Unsupervised Language Model
    Pre-training for French <https://arxiv.org/abs/1912.05372>`__ by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne,
    Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-15. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
+14. :doc:`Funnel Transformer <model_doc/funnel>` (from CMU/Google Brain) released with the paper `Funnel-Transformer:
    Filtering out Sequential Redundancy for Efficient Language Processing <https://arxiv.org/abs/2006.03236>`__ by
    Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-16. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
+15. :doc:`GPT <model_doc/gpt>` (from OpenAI) released with the paper `Improving Language Understanding by Generative
    Pre-Training <https://blog.openai.com/language-unsupervised/>`__ by Alec Radford, Karthik Narasimhan, Tim Salimans
    and Ilya Sutskever.
-17. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
+16. :doc:`GPT-2 <model_doc/gpt2>` (from OpenAI) released with the paper `Language Models are Unsupervised Multitask
    Learners <https://blog.openai.com/better-language-models/>`__ by Alec Radford*, Jeffrey Wu*, Rewon Child, David
    Luan, Dario Amodei** and Ilya Sutskever**.
-18. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
+17. :doc:`LayoutLM <model_doc/layoutlm>` (from Microsoft Research Asia) released with the paper `LayoutLM: Pre-training
    of Text and Layout for Document Image Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li,
    Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-19. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
+18. :doc:`Longformer <model_doc/longformer>` (from AllenAI) released with the paper `Longformer: The Long-Document
    Transformer <https://arxiv.org/abs/2004.05150>`__ by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-20. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
+19. :doc:`LXMERT <model_doc/lxmert>` (from UNC Chapel Hill) released with the paper `LXMERT: Learning Cross-Modality
    Encoder Representations from Transformers for Open-Domain Question Answering <https://arxiv.org/abs/1908.07490>`__
    by Hao Tan and Mohit Bansal.
-21. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
+20. :doc:`MarianMT <model_doc/marian>` Machine translation models trained using `OPUS <http://opus.nlpl.eu/>`__ data by
    Jörg Tiedemann. The `Marian Framework <https://marian-nmt.github.io/>`__ is being developed by the Microsoft
    Translator Team.
-22. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
+21. :doc:`MBart <model_doc/mbart>` (from Facebook) released with the paper `Multilingual Denoising Pre-training for
    Neural Machine Translation <https://arxiv.org/abs/2001.08210>`__ by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li,
    Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-23. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
+22. :doc:`MT5 <model_doc/mt5>` (from Google AI) released with the paper `mT5: A massively multilingual pre-trained
    text-to-text transformer <https://arxiv.org/abs/2010.11934>`__ by Linting Xue, Noah Constant, Adam Roberts, Mihir
    Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-24. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
+23. :doc:`Pegasus <model_doc/pegasus>` (from Google) released with the paper `PEGASUS: Pre-training with Extracted
    Gap-sentences for Abstractive Summarization <https://arxiv.org/abs/1912.08777>`__> by Jingqing Zhang, Yao Zhao,
    Mohammad Saleh and Peter J. Liu.
-25. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
+24. :doc:`ProphetNet <model_doc/prophetnet>` (from Microsoft Research) released with the paper `ProphetNet: Predicting
    Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan, Weizhen Qi,
    Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-26. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
+25. :doc:`Reformer <model_doc/reformer>` (from Google Research) released with the paper `Reformer: The Efficient
    Transformer <https://arxiv.org/abs/2001.04451>`__ by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-27. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
+26. :doc:`RoBERTa <model_doc/roberta>` (from Facebook), released together with the paper a `Robustly Optimized BERT
    Pretraining Approach <https://arxiv.org/abs/1907.11692>`__ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar
    Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. ultilingual BERT into `DistilmBERT
    <https://github.com/huggingface/transformers/tree/master/examples/distillation>`__ and a German version of
    DistilBERT.
-28. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
+27. :doc:`SqueezeBert <model_doc/squeezebert>` released with the paper `SqueezeBERT: What can computer vision teach NLP
    about efficient neural networks? <https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi
    Krishna, and Kurt W. Keutzer.
-29. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
+28. :doc:`T5 <model_doc/t5>` (from Google AI) released with the paper `Exploring the Limits of Transfer Learning with a
    Unified Text-to-Text Transformer <https://arxiv.org/abs/1910.10683>`__ by Colin Raffel and Noam Shazeer and Adam
    Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-30. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
+29. :doc:`Transformer-XL <model_doc/transformerxl>` (from Google/CMU) released with the paper `Transformer-XL:
    Attentive Language Models Beyond a Fixed-Length Context <https://arxiv.org/abs/1901.02860>`__ by Zihang Dai*,
    Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-31. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
+30. :doc:`XLM <model_doc/xlm>` (from Facebook) released together with the paper `Cross-lingual Language Model
    Pretraining <https://arxiv.org/abs/1901.07291>`__ by Guillaume Lample and Alexis Conneau.
-32. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
+31. :doc:`XLM-ProphetNet <model_doc/xlmprophetnet>` (from Microsoft Research) released with the paper `ProphetNet:
    Predicting Future N-gram for Sequence-to-Sequence Pre-training <https://arxiv.org/abs/2001.04063>`__ by Yu Yan,
    Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-33. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
+32. :doc:`XLM-RoBERTa <model_doc/xlmroberta>` (from Facebook AI), released together with the paper `Unsupervised
    Cross-lingual Representation Learning at Scale <https://arxiv.org/abs/1911.02116>`__ by Alexis Conneau*, Kartikay
    Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke
    Zettlemoyer and Veselin Stoyanov.
-34. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
+33. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `XLNet: Generalized Autoregressive
    Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
    Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-35. `Other community models <https://huggingface.co/models>`__, contributed by the `community
+34. `Other community models <https://huggingface.co/models>`__, contributed by the `community
    <https://huggingface.co/users>`__.

-
-The table below represents the current support in the library for each of those models, whether they have a Python
-tokenizer (called "slow"). A "fast" tokenizer backed by the 🤗 Tokenizers library, whether they have support in PyTorch,
-TensorFlow and/or Flax.
-
-..
-    This table is updated automatically from the auto modules with `make fix-copies`. Do not update manually!
-
-.. rst-class:: center-aligned-table
-
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            Model            | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
-+=============================+================+================+=================+====================+==============+
-|           ALBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BART             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            BERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Bert Generation       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Blenderbot          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          CamemBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             DPR             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           DeBERTa           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-| FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|     Funnel Transformer      |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           LXMERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          LayoutLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         MobileBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         ProphetNet          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             RAG             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|           RoBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         SqueezeBERT         |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|         XLM-RoBERTa         |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|        XLMProphetNet        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            XLNet            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-|             mT5             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
-
-
 .. toctree::
    :maxdepth: 2
    :caption: Get started
@@ -325,7 +231,6 @@ TensorFlow and/or Flax.
    model_doc/albert
    model_doc/auto
    model_doc/bart
-    model_doc/barthez
    model_doc/bert
    model_doc/bertgeneration
    model_doc/blenderbot
@@ -97,6 +97,6 @@ You should check out our [swift-coreml-transformers](https://github.com/huggingf
 It contains a set of tools to convert PyTorch or TensorFlow 2.0 trained Transformer models (currently contains `GPT-2`, 
 `DistilGPT-2`, `BERT`, and `DistilBERT`) to CoreML models that run on iOS devices.

-At some point in the future, you'll be able to seamlessly move from pretraining or fine-tuning models in PyTorch or
+At some point in the future, you'll be able to seamlessly move from pre-training or fine-tuning models in PyTorch or
 TensorFlow 2.0 to productizing them in CoreML, or prototype a model or an app in CoreML then research its
 hyperparameters or architecture from PyTorch or TensorFlow 2.0. Super exciting!
@@ -44,8 +44,6 @@ Here is the list of the available :class:`~transformers.TrainerCallback` in the

 .. autoclass:: transformers.ProgressCallback

-.. autoclass:: transformers.EarlyStoppingCallback
-
 .. autoclass:: transformers.integrations.TensorBoardCallback

 .. autoclass:: transformers.integrations.WandbCallback
@@ -1,170 +1,5 @@
 # Migrating from previous packages

-## Migrating from transformers `v3.x` to `v4.x`
-
-A couple of changes were introduced when the switch from version 3 to version 4 was done. Below is a summary of the
-expected changes:
-
-#### 1. AutoTokenizers and pipelines now use fast (rust) tokenizers by default.
-
-The python and rust tokenizers have roughly the same API, but the rust tokenizers have a more complete feature set. 
-
-This introduces two breaking changes:
- The handling of overflowing tokens between the python and rust tokenizers is different.
- The rust tokenizers do not accept integers in the encoding methods.
-
-##### How to obtain the same behavior as v3.x in v4.x
-
- The pipelines now contain additional features out of the box. See the [token-classification pipeline with the `grouped_entities` flag](https://huggingface.co/transformers/main_classes/pipelines.html?highlight=textclassification#tokenclassificationpipeline).
- The auto-tokenizers now return rust tokenizers. In order to obtain the python tokenizers instead, the user may use the `use_fast` flag by setting it to `False`:
-
-In version `v3.x`:
-```py
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-```
-to obtain the same in version `v4.x`:
-```py
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False)
-```
-
-#### 2. SentencePiece is removed from the required dependencies
-
-The requirement on the SentencePiece dependency has been lifted from the `setup.py`. This is done so that we may have a channel on anaconda cloud without relying on `conda-forge`. This means that the tokenizers that depend on the SentencePiece library will not be available with a standard `transformers` installation.
-
-This includes the **slow** versions of:
- `XLNetTokenizer`
- `AlbertTokenizer`
- `CamembertTokenizer`
- `MBartTokenizer`
- `PegasusTokenizer`
- `T5Tokenizer`
- `ReformerTokenizer`
- `XLMRobertaTokenizer`
-
-##### How to obtain the same behavior as v3.x in v4.x
-
-In order to obtain the same behavior as version `v3.x`, you should install `sentencepiece` additionally:
-
-In version `v3.x`:
-```bash
-pip install transformers
-```
-to obtain the same in version `v4.x`:
-```bash
-pip install transformers[sentencepiece]
-```
-or
-```bash
-pip install transformers sentencepiece
-```
-#### 3. The architecture of the repo has been updated so that each model resides in its folder
-
-The past and foreseeable addition of new models means that the number of files in the directory `src/transformers` keeps growing and becomes harder to navigate and understand. We made the choice to put each model and the files accompanying it in their own sub-directories.
-
-This is a breaking change as importing intermediary layers using a model's module directly needs to be done via a different path.
-
-##### How to obtain the same behavior as v3.x in v4.x
-
-In order to obtain the same behavior as version `v3.x`, you should update the path used to access the layers. 
-
-In version `v3.x`:
-```bash
-from transformers.modeling_bert import BertLayer
-```
-to obtain the same in version `v4.x`:
-```bash
-from transformers.models.bert.modeling_bert import BertLayer
-```
-
-#### 4. Switching the `return_dict` argument to `True` by default
-
-The [`return_dict` argument](https://huggingface.co/transformers/main_classes/output.html) enables the return of dict-like python objects containing the model outputs, instead of the standard tuples. This object is self-documented as keys can be used to retrieve values, while also behaving as a tuple as users may retrieve objects by index or by slice.
-
-This is a breaking change as the limitation of that tuple is that it cannot be unpacked: `value0, value1 = outputs` will not work.
-
-##### How to obtain the same behavior as v3.x in v4.x
-
-In order to obtain the same behavior as version `v3.x`, you should specify the `return_dict` argument to `False`, either in the model configuration or during the forward pass.
-
-In version `v3.x`:
-```bash
-model = BertModel.from_pretrained("bert-base-cased")
-outputs = model(**inputs)
-```
-to obtain the same in version `v4.x`:
-```bash
-model = BertModel.from_pretrained("bert-base-cased")
-outputs = model(**inputs, return_dict=False)
-```
-or
-```bash
-model = BertModel.from_pretrained("bert-base-cased", return_dict=False)
-outputs = model(**inputs)
-```
-
-#### 5. Removed some deprecated attributes
-
-Attributes that were deprecated have been removed if they had been deprecated for at least a month. The full list of deprecated attributes can be found in [#8604](https://github.com/huggingface/transformers/pull/8604).
-
-Here is a list of these attributes/methods/arguments and what their replacements should be:
-
-In several models, the labels become consistent with the other models:
- `masked_lm_labels` becomes `labels` in `AlbertForMaskedLM` and `AlbertForPreTraining`.
- `masked_lm_labels` becomes `labels` in `BertForMaskedLM` and `BertForPreTraining`.
- `masked_lm_labels` becomes `labels` in `DistilBertForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `ElectraForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `LongformerForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `MobileBertForMaskedLM`.
- `masked_lm_labels` becomes `labels` in `RobertaForMaskedLM`.
- `lm_labels` becomes `labels` in `BartForConditionalGeneration`.
- `lm_labels` becomes `labels` in `GPT2DoubleHeadsModel`.
- `lm_labels` becomes `labels` in `OpenAIGPTDoubleHeadsModel`.
- `lm_labels` becomes `labels` in `T5ForConditionalGeneration`.
-
-In several models, the caching mechanism becomes consistent with the other models:
- `decoder_cached_states` becomes `past_key_values` in all BART-like, FSMT and T5 models.
- `decoder_past_key_values` becomes `past_key_values` in all BART-like, FSMT and T5 models.
- `past` becomes `past_key_values` in all CTRL models.
- `past` becomes `past_key_values` in all GPT-2 models.
-
-Regarding the tokenizer classes:
- The tokenizer attribute `max_len` becomes `model_max_length`.
- The tokenizer attribute `return_lengths` becomes `return_length`.
- The tokenizer encoding argument `is_pretokenized` becomes `is_split_into_words`.
-
-Regarding the `Trainer` class:
- The `Trainer` argument `tb_writer` is removed in favor of the callback `TensorBoardCallback(tb_writer=...)`.
- The `Trainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
- The `Trainer` attribute `data_collator` should be a callable.
- The `Trainer` method `_log` is deprecated in favor of `log`.
- The `Trainer` method `_training_step` is deprecated in favor of `training_step`.
- The `Trainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
- The `Trainer` method `is_local_master` is deprecated in favor of `is_local_process_zero`.
- The `Trainer` method `is_world_master` is deprecated in favor of `is_world_process_zero`.
-
-Regarding the `TFTrainer` class:
- The `TFTrainer` argument `prediction_loss_only` is removed in favor of the class argument `args.prediction_loss_only`.
- The `Trainer` method `_log` is deprecated in favor of `log`.
- The `TFTrainer` method `_prediction_loop` is deprecated in favor of `prediction_loop`.
- The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`.
- The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`.
-
-Regarding the `TrainerArgument` class:
- The `TrainerArgument` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
-
-Regarding the Transfo-XL model:
- The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`.
- The Transfo-XL modeling method `reset_length` becomes `reset_memory_length`.
-
-Regarding pipelines:
- The `FillMaskPipeline` argument `topk` becomes `top_k`.
-
-
-
 ## Migrating from pytorch-transformers to 🤗 Transformers

 Here is a quick summary of what you should take care of when migrating from `pytorch-transformers` to 🤗 Transformers.
@@ -1,41 +0,0 @@
-BARThez
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The BARThez model was proposed in `BARThez: a Skilled Pretrained French Sequence-to-Sequence Model`
-<https://arxiv.org/abs/2010.12321>`__ by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct,
-2020.
-
-The abstract of the paper:
-
-
-*Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natural Language Processing
-(NLP) field by storm, with models such as BERT and BART setting new state of the art on countless natural language
-understanding tasks. While there are some notable exceptions, most of the available models and research have been
-conducted for the English language. In this work, we introduce BARThez, the first BART model for the French language
-(to the best of our knowledge). BARThez was pretrained on a very large monolingual French corpus from past research
-that we adapted to suit BART's perturbation schemes. Unlike already existing BERT-based French language models such as
-CamemBERT and FlauBERT, BARThez is particularly well-suited for generative tasks, since not only its encoder but also
-its decoder is pretrained. In addition to discriminative tasks from the FLUE benchmark, we evaluate BARThez on a novel
-summarization dataset, OrangeSum, that we release with this paper. We also continue the pretraining of an already
-pretrained multilingual BART on BARThez's corpus, and we show that the resulting model, which we call mBARTHez,
-provides a significant boost over vanilla BARThez, and is on par with or outperforms CamemBERT and FlauBERT.*
-
-The Authors' code can be found `here <https://github.com/moussaKam/BARThez>`__.
-
-
-Examples
-_______________________________________________________________________________________________________________________
-
- BARThez can be fine-tuned on sequence-to-sequence tasks in a similar way as BART, check: `examples/seq2seq/
-  <https://github.com/huggingface/transformers/blob/master/examples/seq2seq/README.md>`__.
-
-
-BarthezTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.BarthezTokenizer
-    :members:
-
@@ -10,7 +10,7 @@ Tasks <https://arxiv.org/abs/1907.12461>`__ by Sascha Rothe, Shashi Narayan, Ali

 The abstract from the paper is the following:

-*Unsupervised pretraining of large neural models has recently revolutionized Natural Language Processing. By
+*Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By
 warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple
 benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language
 Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We
@@ -1,81 +0,0 @@
-CONV_BERT
-----------------------------------------------------------------------------------------------------------------------
-
-Overview
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-The ConvBERT model was proposed in `<INSERT PAPER NAME HERE> <<INSERT PAPER LINK HERE>>`__ by <INSERT AUTHORS HERE>.
-<INSERT SHORT SUMMARY HERE>
-
-The abstract from the paper is the following:
-
-*<INSERT PAPER ABSTRACT HERE>*
-
-Tips:
-
-<INSERT TIPS ABOUT MODEL HERE>
-
-ConvBertConfig
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertConfig
-    :members:
-
-
-ConvBertTokenizer
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertTokenizer
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-ConvBertTokenizerFast
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertTokenizerFast
-    :members: build_inputs_with_special_tokens, get_special_tokens_mask,
-        create_token_type_ids_from_sequences, save_vocabulary
-
-
-ConvBertModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertModel
-    :members: forward
-
-
-ConvBertForMaskedLM
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForMaskedLM
-    :members: forward
-
-
-ConvBertForSequenceClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForSequenceClassification
-    :members: forward
-
-
-ConvBertForMultipleChoice
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForMultipleChoice
-    :members:
-
-
-ConvBertForTokenClassification
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForTokenClassification
-    :members: forward
-
-
-ConvBertForQuestionAnswering
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.ConvBertForQuestionAnswering
-    :members: forward
-
@@ -20,8 +20,8 @@ disentangled attention mechanism, where each word is represented using two vecto
 position, respectively, and the attention weights among words are computed using disentangled matrices on their
 contents and relative positions. Second, an enhanced mask decoder is used to replace the output softmax layer to
 predict the masked tokens for model pretraining. We show that these two techniques significantly improve the efficiency
-of model pretraining and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of
-the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
+of model pre-training and performance of downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half
+of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9%
 (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). The DeBERTa code and
 pre-trained models will be made publicly available at https://github.com/microsoft/DeBERTa.*

@@ -18,9 +18,9 @@ operating these large models in on-the-edge and/or under constrained computation
 remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
 model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
 counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
-knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
+knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by
 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
-biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
+biases learned by larger models during pre-training, we introduce a triple loss combining language modeling,
 distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
 demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
 study.*
@@ -12,14 +12,14 @@ identify which tokens were replaced by the generator in the sequence.

 The abstract from the paper is the following:

-*Masked language modeling (MLM) pretraining methods such as BERT corrupt the input by replacing some tokens with [MASK]
-and then train a model to reconstruct the original tokens. While they produce good results when transferred to
+*Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with
+[MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to
 downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a
-more sample-efficient pretraining task called replaced token detection. Instead of masking the input, our approach
+more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach
 corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead
 of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that
 predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments
-demonstrate this new pretraining task is more efficient than MLM because the task is defined over all input tokens
+demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens
 rather than just the small subset that was masked out. As a result, the contextual representations learned by our
 approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are
 particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained
@@ -19,7 +19,7 @@ representations (Dai and Le, 2015; Peters et al., 2018; Howard and Ruder, 2018;
 heterogeneous French corpus. Models of different sizes are trained using the new CNRS (French National Centre for
 Scientific Research) Jean Zay supercomputer. We apply our French language models to diverse NLP tasks (text
 classification, paraphrasing, natural language inference, parsing, word sense disambiguation) and show that most of the
-time they outperform other pretraining approaches. Different versions of FlauBERT as well as a unified evaluation
+time they outperform other pre-training approaches. Different versions of FlauBERT as well as a unified evaluation
 protocol for the downstream tasks, called FLUE (French Language Understanding Evaluation), are shared to the research
 community for further reproducible experiments in French NLP.*

@@ -14,7 +14,7 @@ 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
+perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training 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
@@ -71,14 +71,14 @@ GPT2Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2Model
-    :members: forward, parallelize, deparallelize
+    :members: forward


 GPT2LMHeadModel
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.GPT2LMHeadModel
-    :members: forward, parallelize, deparallelize
+    :members: forward


 GPT2DoubleHeadsModel
@@ -6,19 +6,19 @@ Overview

 The LayoutLM model was proposed in the paper `LayoutLM: Pre-training of Text and Layout for Document Image
 Understanding <https://arxiv.org/abs/1912.13318>`__ by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and
-Ming Zhou. It's a simple but effective pretraining method of text and layout for document image understanding and
+Ming Zhou. It's a simple but effective pre-training method of text and layout for document image understanding and
 information extraction tasks, such as form understanding and receipt understanding.

 The abstract from the paper is the following:

 *Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the
-widespread use of pretraining models for NLP applications, they almost exclusively focus on text-level manipulation,
+widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation,
 while neglecting layout and style information that is vital for document image understanding. In this paper, we propose
 the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images,
 which is beneficial for a great number of real-world document image understanding tasks such as information extraction
 from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into
 LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single
-framework for document-level pretraining. It achieves new state-of-the-art results in several downstream tasks,
+framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks,
 including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image
 classification (from 93.07 to 94.42).*

@@ -19,7 +19,7 @@ Encoder Representations from Transformers) framework to learn these vision-and-l
 build a large-scale Transformer model that consists of three encoders: an object relationship encoder, a language
 encoder, and a cross-modality encoder. Next, to endow our model with the capability of connecting vision and language
 semantics, we pre-train the model with large amounts of image-and-sentence pairs, via five diverse representative
-pretraining tasks: masked language modeling, masked object prediction (feature regression and label classification),
+pre-training tasks: masked language modeling, masked object prediction (feature regression and label classification),
 cross-modality matching, and image question answering. These tasks help in learning both intra-modality and
 cross-modality relationships. After fine-tuning from our pretrained parameters, our model achieves the state-of-the-art
 results on two visual question answering datasets (i.e., VQA and GQA). We also show the generalizability of our
@@ -13,7 +13,7 @@ The MBart model was presented in `Multilingual Denoising Pre-training for Neural
 Ghazvininejad, Mike Lewis, Luke Zettlemoyer.

 According to the abstract, MBART is a sequence-to-sequence denoising auto-encoder pretrained on large-scale monolingual
-corpora in many languages using the BART objective. mBART is one of the first methods for pretraining a complete
+corpora in many languages using the BART objective. mBART is one of the first methods for pre-training a complete
 sequence-to-sequence model by denoising full texts in multiple languages, while previous approaches have focused only
 on the encoder, decoder, or reconstructing parts of the text.

@@ -39,13 +39,6 @@ MT5ForConditionalGeneration
    :members:


-MT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.MT5EncoderModel
-    :members:
-
-
 TFMT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -58,10 +51,3 @@ TFMT5ForConditionalGeneration

 .. autoclass:: transformers.TFMT5ForConditionalGeneration
    :members:
-
-
-TFMT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFMT5EncoderModel
-    :members:
@@ -17,7 +17,7 @@ the next token.

 The abstract from the paper is the following:

-*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
+*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel
 self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
 the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
 n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
@@ -25,7 +25,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
 overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
 dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
 abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
-state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
+state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*

 The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.

@@ -17,7 +17,7 @@ The abstract from the paper is the following:
 task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning
 has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of
 transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a
-text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transfer
+text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer
 approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration
 with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering
 summarization, question answering, text classification, and more. To facilitate future work on transfer learning for
@@ -99,21 +99,16 @@ T5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5Model
-    :members: forward, parallelize, deparallelize
+    :members: forward


 T5ForConditionalGeneration
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

 .. autoclass:: transformers.T5ForConditionalGeneration
-    :members: forward, parallelize, deparallelize
-
-T5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.T5EncoderModel
    :members: forward

+
 TFT5Model
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

@@ -126,9 +121,3 @@ TFT5ForConditionalGeneration

 .. autoclass:: transformers.TFT5ForConditionalGeneration
    :members: call
-
-TFT5EncoderModel
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.TFT5EncoderModel
-    :members: call
@@ -19,7 +19,7 @@ just the next token. Its architecture is identical to ProhpetNet, but the model

 The abstract from the paper is the following:

-*In this paper, we present a new sequence-to-sequence pretraining model called ProphetNet, which introduces a novel
+*In this paper, we present a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel
 self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism. Instead of
 the optimization of one-step ahead prediction in traditional sequence-to-sequence model, the ProphetNet is optimized by
 n-step ahead prediction which predicts the next n tokens simultaneously based on previous context tokens at each time
@@ -27,7 +27,7 @@ step. The future n-gram prediction explicitly encourages the model to plan for t
 overfitting on strong local correlations. We pre-train ProphetNet using a base scale dataset (16GB) and a large scale
 dataset (160GB) respectively. Then we conduct experiments on CNN/DailyMail, Gigaword, and SQuAD 1.1 benchmarks for
 abstractive summarization and question generation tasks. Experimental results show that ProphetNet achieves new
-state-of-the-art results on all these datasets compared to the models using the same scale pretraining corpus.*
+state-of-the-art results on all these datasets compared to the models using the same scale pre-training corpus.*

 The Authors' code can be found `here <https://github.com/microsoft/ProphetNet>`__.

@@ -527,7 +527,7 @@ Pegasus
 <https://arxiv.org/pdf/1912.08777.pdf>`_, Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.

 Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on
-two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pretraining
+two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pre-training
 objective, called Gap Sentence Generation (GSG).

  * MLM: encoder input tokens are randomly replaced by a mask tokens and have to be predicted by the encoder (like in
@@ -609,7 +609,7 @@ MT5
 `mT5: A massively multilingual pre-trained text-to-text transformer <https://arxiv.org/abs/2010.11934>`_, Linting Xue
 et al.

-The model architecture is same as T5. mT5's pretraining objective includes T5's self-supervised training, but not T5's
+The model architecture is same as T5. mT5's pre-training objective includes T5's self-supervised training, but not T5's
 supervised training. mT5 is trained on 101 languages.

 The library provides a version of this model for conditional generation.
@@ -630,8 +630,8 @@ MBart
 `Multilingual Denoising Pre-training for Neural Machine Translation <https://arxiv.org/abs/2001.08210>`_ by Yinhan Liu,
 Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.

-The model architecture and pretraining objective is same as BART, but MBart is trained on 25 languages and is intended
-for supervised and unsupervised machine translation. MBart is one of the first methods for pretraining a complete
+The model architecture and pre-training objective is same as BART, but MBart is trained on 25 languages and is intended
+for supervised and unsupervised machine translation. MBart is one of the first methods for pre-training a complete
 sequence-to-sequence model by denoising full texts in multiple languages,

 The library provides a version of this model for conditional generation.
@@ -658,7 +658,7 @@ ProphetNet
 `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by
 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou.

-ProphetNet introduces a novel *sequence-to-sequence* pretraining objective, called *future n-gram prediction*. In
+ProphetNet introduces a novel *sequence-to-sequence* pre-training objective, called *future n-gram prediction*. In
 future n-gram prediction, the model predicts the next n tokens simultaneously based on previous context tokens at each
 time step instead instead of just the single next token. The future n-gram prediction explicitly encourages the model
 to plan for the future tokens and prevent overfitting on strong local correlations. The model architecture is based on
@@ -683,8 +683,8 @@ XLM-ProphetNet
 `ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training, <https://arxiv.org/abs/2001.04063>`__ by
 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, Ming Zhou.

-XLM-ProphetNet's model architecture and pretraining objective is same as ProphetNet, but XLM-ProphetNet was pre-trained
-on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.
+XLM-ProphetNet's model architecture and pre-training objective is same as ProphetNet, but XLM-ProphetNet was
+pre-trained on the cross-lingual dataset `XGLUE <https://arxiv.org/abs/2004.01401>`__.

 The library provides a pre-trained version of this model for multi-lingual conditional generation and fine-tuned
 versions for headline generation and question generation, respectively.
@@ -2,6 +2,7 @@ Preprocessing data
 =======================================================================================================================

 In this tutorial, we'll explore how to preprocess your data using 🤗 Transformers. The main tool for this is what we
+
 call a :doc:`tokenizer <main_classes/tokenizer>`. You can build one using the tokenizer class associated to the model
 you would like to use, or directly with the :class:`~transformers.AutoTokenizer` class.

@@ -51,7 +52,7 @@ The tokenizer can decode a list of token ids in a proper sentence:
    "[CLS] Hello, I'm a single sentence! [SEP]"

 As you can see, the tokenizer automatically added some special tokens that the model expects. Not all models need
-special tokens; for instance, if we had used `gpt2-medium` instead of `bert-base-cased` to create our tokenizer, we
+special tokens; for instance, if we had used` gtp2-medium` instead of `bert-base-cased` to create our tokenizer, we
 would have seen the same sentence as the original one here. You can disable this behavior (which is only advised if you
 have added those special tokens yourself) by passing ``add_special_tokens=False``.

@@ -333,12 +333,6 @@ For a list that includes all community-uploaded models, refer to `https://huggin
 |                    | ``facebook/bart-large-cnn``                                | | 24-layer, 1024-hidden, 16-heads, 406M parameters       (same as large)                                                              |
 |                    |                                                            | | bart-large base architecture finetuned on cnn summarization task                                                                    |
 +--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-| BARThez            | ``moussaKam/barthez``                                      | | 12-layer,  768-hidden, 12-heads, 216M parameters                                                                                    |
-|                    |                                                            |                                                                                                                                       |
-|                    |                                                            | (see `details <https://github.com/moussaKam/BARThez>`__)                                                                              |
-|                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
-|                    | ``moussaKam/mbarthez``                                     | | 24-layer, 1024-hidden, 16-heads, 561M parameters                                                                                    |
-+--------------------+------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 | DialoGPT           | ``DialoGPT-small``                                         | | 12-layer, 768-hidden, 12-heads, 124M parameters                                                                                     |
 |                    |                                                            | | Trained on English text: 147M conversation-like exchanges extracted from Reddit.                                                    |
 |                    +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
@@ -240,9 +240,7 @@ activations of the model.
           [ 0.08181786, -0.04179301]], dtype=float32)>,)

 The model can return more than just the final activations, which is why the output is a tuple. Here we only asked for
-the final activations, so we get a tuple with one element.
-
-.. note::
+the final activations, so we get a tuple with one element. .. note::

    All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final activation
    function (like SoftMax) since this final activation function is often fused with the loss.
@@ -70,8 +70,8 @@ inference.
    optimizations afterwards.

 .. note::
-    For more information about the optimizations enabled by ONNXRuntime, please have a look at the `ONNXRuntime Github
-    <https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_.
+    For more information about the optimizations enabled by ONNXRuntime, please have a look at the (`ONNXRuntime Github
+    <https://github.com/microsoft/onnxruntime/tree/master/onnxruntime/python/tools/transformers>`_)

 Quantization
 -----------------------------------------------------------------------------------------------------------------------
@@ -305,7 +305,7 @@ Language modeling is the task of fitting a model to a corpus, which can be domai
 transformer-based models are trained using a variant of language modeling, e.g. BERT with masked language modeling,
 GPT-2 with causal language modeling.

-Language modeling can be useful outside of pretraining as well, for example to shift the model distribution to be
+Language modeling can be useful outside of pre-training as well, for example to shift the model distribution to be
 domain-specific: using a language model trained over a very large corpus, and then fine-tuning it to a news dataset or
 on scientific papers e.g. `LysandreJik/arxiv-nlp <https://huggingface.co/lysandre/arxiv-nlp>`__.

@@ -909,10 +909,9 @@ pipelines), then we should run that test in the non-slow test suite. If it's foc
 such as the documentation or the examples, then we should run these tests in the slow test suite. And then, to refine
 this approach we should have exceptions:

-* All tests that need to download a heavy set of weights or a dataset that is larger than ~50MB (e.g., model or
-  tokenizer integration tests, pipeline integration tests) should be set to slow. If you're adding a new model, you
-  should create and upload to the hub a tiny version of it (with random weights) for integration tests. This is
-  discussed in the following paragraphs.
+* All tests that need to download a heavy set of weights (e.g., model or tokenizer integration tests, pipeline
+  integration tests) should be set to slow. If you're adding a new model, you should create and upload to the hub a
+  tiny version of it (with random weights) for integration tests. This is discussed in the following paragraphs.
 * All tests that need to do a training not specifically optimized to be fast should be set to slow.
 * We can introduce exceptions if some of these should-be-non-slow tests are excruciatingly slow, and set them to
  ``@slow``. Auto-modeling tests, which save and load large files to disk, are a good example of tests that are marked
@@ -93,11 +93,11 @@ class DataTrainingArguments:
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
-    max_seq_length: int = field(
-        default=512,
+    max_seq_length: Optional[int] = field(
+        default=None,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated."
+            "than this will be truncated. Default to the max input length of the model."
        },
    )
    preprocessing_num_workers: Optional[int] = field(
@@ -286,12 +286,15 @@ def main():
            load_from_cache_file=not data_args.overwrite_cache,
        )

-        if data_args.max_seq_length > tokenizer.model_max_length:
-            logger.warn(
-                f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
-                f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
-            )
-        max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
+        if data_args.max_seq_length is None:
+            max_seq_length = tokenizer.model_max_length
+        else:
+            if data_args.max_seq_length > tokenizer.model_max_length:
+                logger.warn(
+                    f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
+                    f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
+                )
+            max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
        # max_seq_length.
@@ -4,9 +4,11 @@ import os
 from pathlib import Path
 from typing import Any, Dict

+import packaging
 import pytorch_lightning as pl
 from pytorch_lightning.utilities import rank_zero_info

+import pkg_resources
 from transformers import (
    AdamW,
    AutoConfig,
@@ -28,12 +30,21 @@ from transformers.optimization import (
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
 )
-from transformers.utils.versions import require_version_examples


 logger = logging.getLogger(__name__)

-require_version_examples("pytorch_lightning>=1.0.4")
+
+def require_min_ver(pkg, min_ver):
+    got_ver = pkg_resources.get_distribution(pkg).version
+    if packaging.version.parse(got_ver) < packaging.version.parse(min_ver):
+        logger.warning(
+            f"{pkg}>={min_ver} is required for a normal functioning of this module, but found {pkg}=={got_ver}. "
+            "Try: pip install -r examples/requirements.txt"
+        )
+
+
+require_min_ver("pytorch_lightning", "1.0.4")

 MODEL_MODES = {
    "base": AutoModel,
@@ -159,81 +159,6 @@ Larger batch size may improve the performance while costing more memory.
 }
 ```

-#### Fine-tuning BERT on SQuAD1.0 with relative position embeddings
-
-The following examples show how to fine-tune BERT models with different relative position embeddings. The BERT model 
-`bert-base-uncased` was pre-trained with default absolute position embeddings. We provide the following pre-trained 
-models which were pre-trained on the same training data (BooksCorpus and English Wikipedia) as in the BERT model 
-training, but with different relative position embeddings. 
-
-* `zhiheng-huang/bert-base-uncased-embedding-relative-key`, trained from scratch with relative embedding proposed by 
-Shaw et al., [Self-Attention with Relative Position Representations](https://arxiv.org/abs/1803.02155)
-* `zhiheng-huang/bert-base-uncased-embedding-relative-key-query`, trained from scratch with relative embedding method 4 
-in Huang et al. [Improve Transformer Models with Better Relative Position Embeddings](https://arxiv.org/abs/2009.13658)
-* `zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query`, fine-tuned from model 
-`bert-large-uncased-whole-word-masking` with 3 additional epochs with relative embedding method 4 in Huang et al. 
-[Improve Transformer Models with Better Relative Position Embeddings](https://arxiv.org/abs/2009.13658)
-
-
-##### Base models fine-tuning
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-output_dir=relative_squad
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
-    --model_type bert \
-    --model_name_or_path zhiheng-huang/bert-base-uncased-embedding-relative-key-query \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --train_file $SQUAD_DIR/train-v1.1.json \
-    --predict_file $SQUAD_DIR/dev-v1.1.json \
-    --learning_rate 3e-5 \
-    --num_train_epochs 2 \
-    --max_seq_length 512 \
-    --doc_stride 128 \
-    --output_dir ${output_dir} \
-    --per_gpu_eval_batch_size=60 \
-    --per_gpu_train_batch_size=6
-```
-Training with the above command leads to the following results. It boosts the BERT default from f1 score of 88.52 to 90.54.
-
-```bash
-'exact': 83.6802270577105, 'f1': 90.54772098174814
-```
-
-The change of `max_seq_length` from 512 to 384 in the above command leads to the f1 score of 90.34. Replacing the above 
-model `zhiheng-huang/bert-base-uncased-embedding-relative-key-query` with 
-`zhiheng-huang/bert-base-uncased-embedding-relative-key` leads to the f1 score of 89.51. The changing of 8 gpus to one 
-gpu training leads to the f1 score of 90.71.
-
-##### Large models fine-tuning
-
-```bash
-export SQUAD_DIR=/path/to/SQUAD
-output_dir=relative_squad
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-python -m torch.distributed.launch --nproc_per_node=8 ./examples/question-answering/run_squad.py \
-    --model_type bert \
-    --model_name_or_path zhiheng-huang/bert-large-uncased-whole-word-masking-embedding-relative-key-query \
-    --do_train \
-    --do_eval \
-    --do_lower_case \
-    --train_file $SQUAD_DIR/train-v1.1.json \
-    --predict_file $SQUAD_DIR/dev-v1.1.json \
-    --learning_rate 3e-5 \
-    --num_train_epochs 2 \
-    --max_seq_length 512 \
-    --doc_stride 128 \
-    --output_dir ${output_dir} \
-    --per_gpu_eval_batch_size=6 \
-    --per_gpu_train_batch_size=2 \
-    --gradient_accumulation_steps 3
-```
-Training with the above command leads to the f1 score of 93.52, which is slightly better than the f1 score of 93.15 for 
-`bert-large-uncased-whole-word-masking`.
-
 ## SQuAD with the Tensorflow Trainer

 ```bash
@@ -153,7 +153,7 @@ def get_args():
    parser.add_argument(
        "--index_name",
        default=None,
-        choices=["exact", "compressed", "legacy"],
+        choices=["hf", "legacy"],
        type=str,
        help="RAG model retriever type",
    )
@@ -203,30 +203,30 @@ model = AutoModelForSeq2SeqLM.from_pretrained(f'{output_dir}/best_tfmr')
 ```

 ### Fine-tuning using Seq2SeqTrainer
-To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer`-related `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument.
+To use `Seq2SeqTrainer` for fine-tuning you should use the `finetune_trainer.py` script. It subclasses `Trainer` to extend it for seq2seq training. Except the `Trainer` releated `TrainingArguments`, it shares the same argument names as that of `finetune.py` file. One notable difference is that, calculating generative metrics (BLEU, ROUGE) is optional and is controlled using the `--predict_with_generate` argument, set this argument to calculate BLEU and ROUGE metrics.

 With PyTorch 1.6+ it'll automatically use `native AMP` when `--fp16` is set.

 To see all the possible command line options, run:

 ```bash
-python finetune_trainer.py --help
+./builtin_trainer/finetune.sh --help # This calls python finetune_trainer.py --help
 ```

 **At the moment, `Seq2SeqTrainer` does not support *with teacher* distillation.**

-All `Seq2SeqTrainer`-based fine-tuning scripts are included in the `builtin_trainer` directory.
+All `Seq2SeqTrainer` based fine-tuning scripts are included in the `builtin_trainer` directory.

 #### TPU Training
 `Seq2SeqTrainer` supports TPU training with few caveats
-1. As `generate` method does not work on TPU at the moment, `predict_with_generate` cannot be used. You should use `--prediction_loss_only` to only calculate loss, and do not set `--do_predict` and `--predict_with_generate`.
-2. All sequences should be padded to be of equal length to avoid extremely slow training. (`finetune_trainer.py` does this automatically when running on TPU.)
+1. As `generate` method does not work on TPU at the moment, `predict_with_generate` can not be used. You should use `--prediction_loss_only` to only calculate loss, and do not set `--do_predict` and `--predict_with_generate`.
+2. All sequences should be padded to be of equal length otherwise it leads to extremely slow training. (`finetune_trainer.py` does this automatically when running on TPU.)

-We provide a very simple launcher script named `xla_spawn.py` that lets you run our example scripts on multiple TPU cores without any boilerplate. Just pass a `--num_cores` flag to this script, then your regular training script with its arguments (this is similar to the `torch.distributed.launch` helper for `torch.distributed`).
+We provide a very simple launcher script named `xla_spawn.py` that lets you run our example scripts on multiple TPU cores without any boilerplate. Just pass a --num_cores flag to this script, then your regular training script with its arguments (this is similar to the torch.distributed.launch helper for torch.distributed).

 `builtin_trainer/finetune_tpu.sh` script provides minimal arguments needed for TPU training.

-The following command fine-tunes `sshleifer/student_marian_en_ro_6_3` on TPU V3-8 and should complete one epoch in ~5-6 mins.
+Following command fine-tunes `sshleifer/student_marian_en_ro_6_3` on TPU V3-8 and should complete one epoch in ~5-6 mins.

 ```bash
 ./builtin_trainer/train_distil_marian_enro_tpu.sh
@@ -16,6 +16,6 @@ python finetune_trainer.py \
    --num_train_epochs 6 \
    --save_steps 25000 --eval_steps 25000 --logging_steps 1000 \
    --do_train --do_eval --do_predict --evaluate_during_training \
-    --predict_with_generate --logging_first_step \
+    --predict_with_generate --logging_first_step 
    --task translation \
    "$@"
@@ -154,8 +154,7 @@ def perturb_past(
        # Compute hidden using perturbed past
        perturbed_past = list(map(add, past, curr_perturbation))
        _, _, _, curr_length, _ = curr_perturbation[0].shape
-        lm_output = model(last, past_key_values=perturbed_past)
-        all_logits, all_hidden = lm_output["logits"], lm_output["hidden_states"]
+        all_logits, _, all_hidden = model(last, past=perturbed_past)
        hidden = all_hidden[-1]
        new_accumulated_hidden = accumulated_hidden + torch.sum(hidden, dim=1).detach()
        # TODO: Check the layer-norm consistency of this with trained discriminator (Sumanth)
@@ -180,8 +179,7 @@ def perturb_past(
            wte = model.resize_token_embeddings()
            for _ in range(horizon_length):
                inputs_embeds = torch.matmul(curr_probs, wte.weight.data)
-                lm_output = model(past_key_values=curr_unpert_past, inputs_embeds=inputs_embeds)
-                curr_unpert_past, curr_all_hidden = lm_output["past_key_values"], lm_output["hidden_states"]
+                _, curr_unpert_past, curr_all_hidden = model(past=curr_unpert_past, inputs_embeds=inputs_embeds)
                curr_hidden = curr_all_hidden[-1]
                new_accumulated_hidden = new_accumulated_hidden + torch.sum(curr_hidden, dim=1)

@@ -464,14 +462,9 @@ def generate_text_pplm(
        if past is None and output_so_far is not None:
            last = output_so_far[:, -1:]
            if output_so_far.shape[1] > 1:
-                past = model(output_so_far[:, :-1])["past_key_values"]
+                _, past, _ = model(output_so_far[:, :-1])

-        lm_output = model(output_so_far)
-        unpert_logits, unpert_past, unpert_all_hidden = (
-            lm_output["logits"],
-            lm_output["past_key_values"],
-            lm_output["hidden_states"],
-        )
+        unpert_logits, unpert_past, unpert_all_hidden = model(output_so_far)
        unpert_last_hidden = unpert_all_hidden[-1]

        # check if we are abowe grad max length
@@ -514,11 +507,7 @@ def generate_text_pplm(
            else:
                pert_past = past

-        lm_output = model(last, past_key_values=pert_past)
-        pert_logits, past = (
-            lm_output["logits"],
-            lm_output["past_key_values"],
-        )
+        pert_logits, past, pert_all_hidden = model(last, past=pert_past)
        pert_logits = pert_logits[:, -1, :] / temperature  # + SMALL_CONST

        for token_idx in set(output_so_far[0].tolist()):
@@ -64,7 +64,7 @@ class Discriminator(torch.nn.Module):

    def avg_representation(self, x):
        mask = x.ne(0).unsqueeze(2).repeat(1, 1, self.embed_size).float().to(self.device).detach()
-        hidden = self.encoder.transformer(x)["last_hidden_state"]
+        hidden, _ = self.encoder.transformer(x)
        masked_hidden = hidden * mask
        avg_hidden = torch.sum(masked_hidden, dim=1) / (torch.sum(mask, dim=1).detach() + EPSILON)
        return avg_hidden
@@ -1,20 +0,0 @@
---
-language: ja
-license: apache-2.0
---
-
-## Japanese ELECTRA-small
-
-We provide a Japanese **ELECTRA-Small** model, as described in [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
-
-Our pretraining process employs subword units derived from the [Japanese Wikipedia](https://dumps.wikimedia.org/jawiki/latest), using the [Byte-Pair Encoding](https://www.aclweb.org/anthology/P16-1162.pdf) method and building on an initial tokenization with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd). For optimal performance, please take care to set your MeCab dictionary appropriately.
-
-## How to use the discriminator in `transformers`
-
-```
-from transformers import BertJapaneseTokenizer, ElectraForPreTraining
-
-tokenizer = BertJapaneseTokenizer.from_pretrained('Cinnamon/electra-small-japanese-discriminator', mecab_kwargs={"mecab_option": "-d /usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd"})
-
-model = ElectraForPreTraining.from_pretrained('Cinnamon/electra-small-japanese-discriminator')
-```
@@ -1,6 +1,6 @@
 ---
 language: eo
-thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
+thumbnail: https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png
 widget:
 - text: "Mi estas viro kej estas tago varma."
 ---
@@ -15,7 +15,7 @@ widget:
 - machine name: `galinette`


-![](https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png)
+![](https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png)

 ## Example pipeline

@@ -1,6 +1,6 @@
 ---
 language: eo
-thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
+thumbnail: https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png
 widget:
 - text: "Jen la komenco de bela <mask>."
 - text: "Uno du <mask>"
@@ -17,7 +17,7 @@ widget:
 - machine name: `galinette`


-![](https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png)
+![](https://huggingface.co/blog/assets/EsperBERTo-thumbnail-v2.png)

 ## Example pipeline

@@ -1,52 +0,0 @@
---
-language: en
-license: apache-2.0
-datasets:
- cnn_dailymail
-tags:
- summarization
---
-
-# Bert-mini2Bert-mini Summarization with 🤗EncoderDecoder Framework
-
-This model is a warm-started *BERT2BERT* ([mini](https://huggingface.co/google/bert_uncased_L-4_H-256_A-4)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
-
-The model achieves a **16.51** ROUGE-2 score on *CNN/Dailymail*'s test dataset.
-
-For more details on how the model was fine-tuned, please refer to 
-[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.
-
-## Results on test set 📝
-
-| Metric | # Value   |
-| ------ | --------- |
-| **ROUGE-2** | **16.51** |
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import BertTokenizerFast, EncoderDecoderModel
-import torch
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization')
-model = EncoderDecoderModel.from_pretrained('mrm8488/bert-mini2bert-mini-finetuned-cnn_daily_mail-summarization').to(device)
-
-def generate_summary(text):
-    # cut off at BERT max length 512
-    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
-    input_ids = inputs.input_ids.to(device)
-    attention_mask = inputs.attention_mask.to(device)
-
-    output = model.generate(input_ids, attention_mask=attention_mask)
-
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-  
-text = "your text to be summarized here..."
-generate_summary(text)
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain
@@ -1,52 +0,0 @@
---
-language: en
-license: apache-2.0
-datasets:
- cnn_dailymail
-tags:
- summarization
---
-
-# Bert-small2Bert-small Summarization with 🤗EncoderDecoder Framework
-
-This model is a warm-started *BERT2BERT* ([small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)) model fine-tuned on the *CNN/Dailymail* summarization dataset.
-
-The model achieves a **17.37** ROUGE-2 score on *CNN/Dailymail*'s test dataset.
-
-For more details on how the model was fine-tuned, please refer to 
-[this](https://colab.research.google.com/drive/1Ekd5pUeCX7VOrMx94_czTkwNtLN32Uyu?usp=sharing) notebook.
-
-## Results on test set 📝
-
-| Metric | # Value   |
-| ------ | --------- |
-| **ROUGE-2** | **17.37** |
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import BertTokenizerFast, EncoderDecoderModel
-import torch
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = BertTokenizerFast.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization')
-model = EncoderDecoderModel.from_pretrained('mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization').to(device)
-
-def generate_summary(text):
-    # cut off at BERT max length 512
-    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
-    input_ids = inputs.input_ids.to(device)
-    attention_mask = inputs.attention_mask.to(device)
-
-    output = model.generate(input_ids, attention_mask=attention_mask)
-
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-  
-text = "your text to be summarized here..."
-generate_summary(text)
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain
@@ -1,85 +0,0 @@
---
-language: multilingual
-datasets:
- tydiqa
-pipeline_tag: question-answering
---
-
-# mT5-small fine-tuned on TyDiQA for multilingual QA 🗺📖❓
-[Google's mT5-small](https://huggingface.co/google/mt5-small) fine-tuned on [TyDi QA](https://huggingface.co/nlp/viewer/?dataset=tydiqa&config=secondary_task) (secondary task) for **multingual Q&A** downstream task.
-
-## Details of mT5
-
-[Google's mT5](https://github.com/google-research/multilingual-t5)
-
-mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages:
-
-Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu.
-
-**Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task.
-
-Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual)
-
-Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5)
-
-Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)
-
-Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel* 
-
-
-## Details of the dataset 📚 
-
-**TyDi QA** is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. The languages of TyDi QA are diverse with regard to their typology -- the set of linguistic features that each language expresses -- such that we expect models performing well on this set to generalize across a large number of the languages in the world. It contains language phenomena that would not be found in English-only corpora. To provide a realistic information-seeking task and avoid priming effects, questions are written by people who want to know the answer, but don’t know the answer yet, (unlike SQuAD and its descendents) and the data is collected directly in each language without the use of translation (unlike MLQA and XQuAD).
-
-| Dataset  | Task | Split | # samples |
-| -------- | ----- |------| --------- |
-| TyDi QA | GoldP  | train| 49881     |
-| TyDi QA | GoldP  | valid| 5077      | 
-
-
-
-## Results on validation dataset 📝
-
-| Metric | # Value   |
-| ------ | --------- |
-| **EM** | **41.65** |
-
-
-
-## Model in Action 🚀
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/mT5-small-finetuned-tydiqa-for-xqa")
-model = AutoModelForCausalLM.from_pretrained("mrm8488/mT5-small-finetuned-tydiqa-for-xqa").to(device)
-
-def get_response(question, context, max_length=32):
-  input_text = 'question: %s  context: %s' % (question, context)
-  features = tokenizer([input_text], return_tensors='pt')
-
-  output = model.generate(input_ids=features['input_ids'].to(device), 
-               attention_mask=features['attention_mask'].to(device),
-               max_length=max_length)
-
-  return tokenizer.decode(output[0])
-  
-# Some examples in different languages
-
-context = 'HuggingFace won the best Demo paper at EMNLP2020.'
-question = 'What won HuggingFace?'
-get_response(question, context)
-
-context = 'HuggingFace ganó la mejor demostración con su paper en la EMNLP2020.'
-question = 'Qué ganó HuggingFace?'
-get_response(question, context)
-
-context = 'HuggingFace выиграл лучшую демонстрационную работу на EMNLP2020.'
-question = 'Что победило в HuggingFace?'
-get_response(question, context)
-```
-
-> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
-
-> Made with <span style="color: #e25555;">&hearts;</span> in Spain
@@ -91,7 +91,7 @@ print(tokenizer_greek.convert_ids_to_tokens(outputs[0, 5].max(0)[1].item()))
 # ================ EXAMPLE 2 ================
 text_2 = 'Είναι ένας [MASK] άνθρωπος.'
 # EN: 'He is a [MASK] person.'
-input_ids = tokenizer_greek.encode(text_2)
+input_ids = tokenizer_greek.encode(text_1)
 print(tokenizer_greek.convert_ids_to_tokens(input_ids))
 # ['[CLS]', 'ειναι', 'ενας', '[MASK]', 'ανθρωπος', '.', '[SEP]']
 outputs = lm_model_greek(torch.tensor([input_ids]))[0]
@@ -47,9 +47,7 @@ To create the package for pypi.
 """

 import os
-import re
 import shutil
-from distutils.core import Command
 from pathlib import Path

 from setuptools import find_packages, setup
@@ -71,163 +69,54 @@ if stale_egg_info.exists():
    shutil.rmtree(stale_egg_info)


-# IMPORTANT:
-# 1. all dependencies should be listed here with their version requirements if any
-# 2. once modified, run: `make deps_table_update` to update src/transformers/dependency_versions_table.py
-_deps = [
-    "black>=20.8b1",
-    "cookiecutter==1.7.2",
-    "dataclasses",
-    "datasets",
-    "faiss-cpu",
-    "fastapi",
-    "filelock",
-    "flake8>=3.8.3",
-    "flax==0.2.2",
-    "fugashi>=1.0",
-    "ipadic>=1.0.0,<2.0",
-    "isort>=5.5.4",
-    "jax>=0.2.0",
-    "jaxlib==0.1.55",
-    "keras2onnx",
-    "numpy",
-    "onnxconverter-common",
-    "onnxruntime-tools>=1.4.2",
-    "onnxruntime>=1.4.0",
-    "packaging",
-    "parameterized",
-    "protobuf",
-    "psutil",
-    "pydantic",
-    "pytest",
-    "pytest-xdist",
-    "python>=3.6.0",
-    "recommonmark",
-    "regex!=2019.12.17",
-    "requests",
-    "sacremoses",
-    "scikit-learn",
-    "sentencepiece==0.1.91",
-    "sphinx-copybutton",
-    "sphinx-markdown-tables",
-    "sphinx-rtd-theme==0.4.3",  # sphinx-rtd-theme==0.5.0 introduced big changes in the style.
-    "sphinx==3.2.1",
-    "starlette",
-    "tensorflow-cpu>=2.0",
-    "tensorflow>=2.0",
-    "timeout-decorator",
-    "tokenizers==0.9.4",
-    "torch>=1.0",
-    "tqdm>=4.27",
-    "unidic>=1.0.2",
-    "unidic_lite>=1.0.7",
-    "uvicorn",
-]
-
-
-# tokenizers: "tokenizers==0.9.4" lookup table
-# support non-versions file too so that they can be checked at run time
-deps = {b: a for a, b in (re.findall(r"^(([^!=<>]+)(?:[!=<>].*)?$)", x)[0] for x in _deps)}
-
-
-def deps_list(*pkgs):
-    return [deps[pkg] for pkg in pkgs]
-
-
-class DepsTableUpdateCommand(Command):
-    """
-    A custom distutils command that updates the dependency table.
-    usage: python setup.py deps_table_update
-    """
-
-    description = "build runtime dependency table"
-    user_options = [
-        # format: (long option, short option, description).
-        ("dep-table-update", None, "updates src/transformers/dependency_versions_table.py"),
-    ]
-
-    def initialize_options(self):
-        pass
-
-    def finalize_options(self):
-        pass
-
-    def run(self):
-        entries = "\n".join([f'    "{k}": "{v}",' for k, v in deps.items()])
-        content = [
-            "# THIS FILE HAS BEEN AUTOGENERATED. To update:",
-            "# 1. modify the `_deps` dict in setup.py",
-            "# 2. run `make deps_table_update``",
-            "deps = {",
-            entries,
-            "}",
-            ""
-        ]
-        target = "src/transformers/dependency_versions_table.py"
-        print(f"updating {target}")
-        with open(target, "w", encoding="utf-8", newline="\n") as f:
-            f.write("\n".join(content))
-
-
 extras = {}

-extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic")
-extras["sklearn"] = deps_list("scikit-learn")
+extras["ja"] = ["fugashi>=1.0", "ipadic>=1.0.0,<2.0", "unidic_lite>=1.0.7", "unidic>=1.0.2"]
+extras["sklearn"] = ["scikit-learn"]

-extras["tf"] = deps_list("tensorflow", "onnxconverter-common", "keras2onnx")
-extras["tf-cpu"] = deps_list("tensorflow-cpu", "onnxconverter-common", "keras2onnx")
-
-extras["torch"] = deps_list("torch")
+# keras2onnx and onnxconverter-common version is specific through a commit until 1.7.0 lands on pypi
+extras["tf"] = [
+    "tensorflow>=2.0",
+    "onnxconverter-common",
+    "keras2onnx"
+    # "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
+    # "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx",
+]
+extras["tf-cpu"] = [
+    "tensorflow-cpu>=2.0",
+    "onnxconverter-common",
+    "keras2onnx"
+    # "onnxconverter-common @ git+git://github.com/microsoft/onnxconverter-common.git@f64ca15989b6dc95a1f3507ff6e4c395ba12dff5#egg=onnxconverter-common",
+    # "keras2onnx @ git+git://github.com/onnx/keras-onnx.git@cbdc75cb950b16db7f0a67be96a278f8d2953b48#egg=keras2onnx",
+]
+extras["torch"] = ["torch>=1.0"]

 if os.name == "nt":  # windows
-    extras["retrieval"] = deps_list("datasets")  # faiss is not supported on windows
-    extras["flax"] = []  # jax is not supported on windows
+    extras["retrieval"] = ["datasets"] # faiss is not supported on windows
+    extras["flax"] = [] # jax is not supported on windows
 else:
-    extras["retrieval"] = deps_list("faiss-cpu", "datasets")
-    extras["flax"] = deps_list("jax", "jaxlib", "flax")
+    extras["retrieval"] = ["faiss-cpu", "datasets"]
+    extras["flax"] = ["jaxlib==0.1.55", "jax>=0.2.0", "flax==0.2.2"]

-extras["tokenizers"] = deps_list("tokenizers")
-extras["onnxruntime"] = deps_list("onnxruntime", "onnxruntime-tools")
-extras["modelcreation"] = deps_list("cookiecutter")
+extras["tokenizers"] = ["tokenizers==0.9.4"]
+extras["onnxruntime"] = ["onnxruntime>=1.4.0", "onnxruntime-tools>=1.4.2"]
+extras["modelcreation"] = ["cookiecutter==1.7.2"]

-extras["serving"] = deps_list("pydantic", "uvicorn", "fastapi", "starlette")
+extras["serving"] = ["pydantic", "uvicorn", "fastapi", "starlette"]
+
+extras["sentencepiece"] = ["sentencepiece==0.1.91", "protobuf"]
+extras["retrieval"] = ["faiss-cpu", "datasets"]
+extras["testing"] = ["pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil"] + extras["retrieval"] + extras["modelcreation"]
+# sphinx-rtd-theme==0.5.0 introduced big changes in the style.
+extras["docs"] = ["recommonmark", "sphinx==3.2.1", "sphinx-markdown-tables", "sphinx-rtd-theme==0.4.3", "sphinx-copybutton"]
+extras["quality"] = ["black >= 20.8b1", "isort >= 5.5.4", "flake8 >= 3.8.3"]

-extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
-extras["retrieval"] = deps_list("faiss-cpu", "datasets")
-extras["testing"] = (
-    deps_list("pytest", "pytest-xdist", "timeout-decorator", "parameterized", "psutil")
-    + extras["retrieval"]
-    + extras["modelcreation"]
-)
-extras["docs"] = deps_list("recommonmark", "sphinx", "sphinx-markdown-tables", "sphinx-rtd-theme", "sphinx-copybutton")
-extras["quality"] = deps_list("black", "isort", "flake8")

 extras["all"] = extras["tf"] + extras["torch"] + extras["flax"] + extras["sentencepiece"] + extras["tokenizers"]

-extras["dev"] = (
-    extras["all"]
-    + extras["testing"]
-    + extras["quality"]
-    + extras["ja"]
-    + extras["docs"]
-    + extras["sklearn"]
-    + extras["modelcreation"]
-)
+extras["dev"] = extras["all"] + extras["testing"] + extras["quality"] + extras["ja"] + extras["docs"] + extras["sklearn"] + extras["modelcreation"]


-# when modifying the following list, make sure to update src/transformers/dependency_versions_check.py
-install_requires = [
-    deps["dataclasses"] + ";python_version<'3.7'",  # dataclasses for Python versions that don't have it
-    deps["filelock"],   # filesystem locks, e.g., to prevent parallel downloads
-    deps["numpy"],
-    deps["packaging"],  # utilities from PyPA to e.g., compare versions
-    deps["regex"],      # for OpenAI GPT
-    deps["requests"],   # for downloading models over HTTPS
-    deps["sacremoses"], # for XLM
-    deps["tokenizers"],
-    deps["tqdm"],       # progress bars in model download and training scripts
-]
-
 setup(
    name="transformers",
    version="4.0.0-rc-1",
@@ -241,10 +130,27 @@ setup(
    url="https://github.com/huggingface/transformers",
    package_dir={"": "src"},
    packages=find_packages("src"),
+    install_requires=[
+        "numpy",
+        "tokenizers == 0.9.4",
+        # dataclasses for Python versions that don't have it
+        "dataclasses;python_version<'3.7'",
+        # utilities from PyPA to e.g. compare versions
+        "packaging",
+        # filesystem locks e.g. to prevent parallel downloads
+        "filelock",
+        # for downloading models over HTTPS
+        "requests",
+        # progress bars in model download and training scripts
+        "tqdm >= 4.27",
+        # for OpenAI GPT
+        "regex != 2019.12.17",
+        # for XLM
+        "sacremoses",
+    ],
    extras_require=extras,
    entry_points={"console_scripts": ["transformers-cli=transformers.commands.transformers_cli:main"]},
    python_requires=">=3.6.0",
-    install_requires=install_requires,
    classifiers=[
        "Development Status :: 5 - Production/Stable",
        "Intended Audience :: Developers",
@@ -257,5 +163,4 @@ setup(
        "Programming Language :: Python :: 3.7",
        "Topic :: Scientific/Engineering :: Artificial Intelligence",
    ],
-    cmdclass={"deps_table_update": DepsTableUpdateCommand},
 )
@@ -17,7 +17,15 @@ else:
    absl.logging.set_stderrthreshold("info")
    absl.logging._warn_preinit_stderr = False

-from . import dependency_versions_check
+# Integrations: this needs to come before other ml imports
+# in order to allow any 3rd-party code to initialize properly
+from .integrations import (  # isort:skip
+    is_comet_available,
+    is_optuna_available,
+    is_ray_available,
+    is_tensorboard_available,
+    is_wandb_available,
+)

 # Configuration
 from .configuration_utils import PretrainedConfig
@@ -47,6 +55,7 @@ from .data import (
 # Files and general utilities
 from .file_utils import (
    CONFIG_NAME,
+    TOKENIZER_CONFIG_NAME,
    MODEL_CARD_NAME,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
@@ -90,33 +99,34 @@ from .models.albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
 from .models.auto import (
    ALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CONFIG_MAPPING,
-    MODEL_NAMES_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoTokenizer,
 )
-from .models.bart import BartConfig, BartTokenizer
+from .models.bart import BartConfig, BartTokenizer, BART_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.bert import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    BERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
    BasicTokenizer,
    BertConfig,
    BertTokenizer,
    WordpieceTokenizer,
 )
 from .models.bert_generation import BertGenerationConfig
-from .models.bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
-from .models.bertweet import BertweetTokenizer
+from .models.bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer, BERT_JAPANESE_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.bertweet import BertweetTokenizer, BERT_TWEET_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.blenderbot import (
    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    BLENDERBOT_LARGE_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
+    BLENDERBOT_SMALL_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
    BlenderbotConfig,
    BlenderbotSmallTokenizer,
    BlenderbotTokenizer,
 )
 from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
-from .models.conv_bert import CONV_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig
-from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer
-from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer
-from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer
+from .models.ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig, CTRLTokenizer, CTRL_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaTokenizer, DEBERTA_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig, DistilBertTokenizer, DISTILBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.dpr import (
    DPR_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPRConfig,
@@ -124,23 +134,26 @@ from .models.dpr import (
    DPRQuestionEncoderTokenizer,
    DPRReaderOutput,
    DPRReaderTokenizer,
+        DPR_CONTEXT_ENCODER_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
+        DPR_QUESTION_ENCODER_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
+        DPR_READER_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 )
-from .models.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer
+from .models.electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraTokenizer, ELECTRA_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.encoder_decoder import EncoderDecoderConfig
-from .models.flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertTokenizer
-from .models.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer
-from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer
-from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer
-from .models.herbert import HerbertTokenizer
-from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer
-from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer
-from .models.lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig, LxmertTokenizer
+from .models.flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertTokenizer, FLAUBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig, FSMTTokenizer, FSMT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig, FunnelTokenizer, FUNNEL_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config, GPT2Tokenizer, GPT2_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.herbert import HerbertTokenizer, HERBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMTokenizer, LAYOUTLM_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig, LongformerTokenizer, LONGFORMER_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+from .models.lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig, LxmertTokenizer, LXMERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.marian import MarianConfig
 from .models.mbart import MBartConfig
 from .models.mmbt import MMBTConfig
-from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer
+from .models.mobilebert import MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileBertConfig, MobileBertTokenizer, MOBILEBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.mt5 import MT5Config
-from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer
+from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer, OPENAIGPT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
 from .models.pegasus import PegasusConfig
 from .models.phobert import PhobertTokenizer
 from .models.prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig, ProphetNetTokenizer
@@ -197,24 +210,12 @@ from .tokenization_utils_base import (
 )


-# Integrations: this needs to come before other ml imports
-# in order to allow any 3rd-party code to initialize properly
-from .integrations import (  # isort:skip
-    is_comet_available,
-    is_optuna_available,
-    is_ray_available,
-    is_tensorboard_available,
-    is_wandb_available,
-)
-
-
 if is_sentencepiece_available():
-    from .models.albert import AlbertTokenizer
-    from .models.barthez import BarthezTokenizer
-    from .models.bert_generation import BertGenerationTokenizer
-    from .models.camembert import CamembertTokenizer
-    from .models.marian import MarianTokenizer
-    from .models.mbart import MBartTokenizer
+    from .models.albert import AlbertTokenizer, ALBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.bert_generation import BertGenerationTokenizer, BERT_GENERATION_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.camembert import CamembertTokenizer, CAMEMBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.marian import MarianTokenizer, MARIAN_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.mbart import MBartTokenizer, MBART_PRETRAINED_TOKENIZER_ARCHIVE_LIST
    from .models.pegasus import PegasusTokenizer
    from .models.reformer import ReformerTokenizer
    from .models.t5 import T5Tokenizer
@@ -225,23 +226,25 @@ else:
    from .utils.dummy_sentencepiece_objects import *

 if is_tokenizers_available():
-    from .models.albert import AlbertTokenizerFast
-    from .models.bart import BartTokenizerFast
-    from .models.barthez import BarthezTokenizerFast
-    from .models.bert import BertTokenizerFast
-    from .models.camembert import CamembertTokenizerFast
-    from .models.distilbert import DistilBertTokenizerFast
-    from .models.dpr import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast, DPRReaderTokenizerFast
-    from .models.electra import ElectraTokenizerFast
-    from .models.funnel import FunnelTokenizerFast
-    from .models.gpt2 import GPT2TokenizerFast
-    from .models.herbert import HerbertTokenizerFast
-    from .models.layoutlm import LayoutLMTokenizerFast
-    from .models.longformer import LongformerTokenizerFast
-    from .models.lxmert import LxmertTokenizerFast
-    from .models.mbart import MBartTokenizerFast
-    from .models.mobilebert import MobileBertTokenizerFast
-    from .models.openai import OpenAIGPTTokenizerFast
+    from .models.albert import AlbertTokenizerFast, ALBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.bart import BartTokenizerFast, BART_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.bert import BertTokenizerFast, BERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.camembert import CamembertTokenizerFast, CAMEMBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.distilbert import DistilBertTokenizerFast, DISTILBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.dpr import (DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast, DPRReaderTokenizerFast,        DPR_CONTEXT_ENCODER_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
+        DPR_QUESTION_ENCODER_PRETRAINED_TOKENIZER_ARCHIVE_LIST,
+        DPR_READER_PRETRAINED_TOKENIZER_ARCHIVE_LIST)
+
+    from .models.electra import ElectraTokenizerFast, ELECTRA_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.funnel import FunnelTokenizerFast, FUNNEL_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.gpt2 import GPT2TokenizerFast, GPT2_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.herbert import HerbertTokenizerFast, HERBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.layoutlm import LayoutLMTokenizerFast, LAYOUTLM_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.longformer import LongformerTokenizerFast, LONGFORMER_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.lxmert import LxmertTokenizerFast, LXMERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.mbart import MBartTokenizerFast, MBART_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.mobilebert import MobileBertTokenizerFast, MOBILEBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
+    from .models.openai import OpenAIGPTTokenizerFast, OPENAIGPT_PRETRAINED_TOKENIZER_ARCHIVE_LIST
    from .models.pegasus import PegasusTokenizerFast
    from .models.reformer import ReformerTokenizerFast
    from .models.retribert import RetriBertTokenizerFast
@@ -260,7 +263,6 @@ else:
 # Trainer
 from .trainer_callback import (
    DefaultFlowCallback,
-    EarlyStoppingCallback,
    PrinterCallback,
    ProgressCallback,
    TrainerCallback,
@@ -392,18 +394,6 @@ if is_torch_available():
        CamembertForTokenClassification,
        CamembertModel,
    )
-    from .models.conv_bert import (
-        CONV_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
-        ConvBertForMaskedLM,
-        ConvBertForMultipleChoice,
-        ConvBertForQuestionAnswering,
-        ConvBertForSequenceClassification,
-        ConvBertForTokenClassification,
-        ConvBertLayer,
-        ConvBertModel,
-        ConvBertPreTrainedModel,
-        load_tf_weights_in_conv_bert,
-    )
    from .models.ctrl import CTRL_PRETRAINED_MODEL_ARCHIVE_LIST, CTRLLMHeadModel, CTRLModel, CTRLPreTrainedModel
    from .models.deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
@@ -519,7 +509,7 @@ if is_torch_available():
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )
-    from .models.mt5 import MT5EncoderModel, MT5ForConditionalGeneration, MT5Model
+    from .models.mt5 import MT5ForConditionalGeneration, MT5Model
    from .models.openai import (
        OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        OpenAIGPTDoubleHeadsModel,
@@ -574,7 +564,6 @@ if is_torch_available():
    )
    from .models.t5 import (
        T5_PRETRAINED_MODEL_ARCHIVE_LIST,
-        T5EncoderModel,
        T5ForConditionalGeneration,
        T5Model,
        T5PreTrainedModel,
@@ -817,7 +806,7 @@ if is_tf_available():
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )
-    from .models.mt5 import TFMT5EncoderModel, TFMT5ForConditionalGeneration, TFMT5Model
+    from .models.mt5 import TFMT5ForConditionalGeneration, TFMT5Model
    from .models.openai import (
        TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFOpenAIGPTDoubleHeadsModel,
@@ -840,7 +829,6 @@ if is_tf_available():
    )
    from .models.t5 import (
        TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
-        TFT5EncoderModel,
        TFT5ForConditionalGeneration,
        TFT5Model,
        TFT5PreTrainedModel,
@@ -898,7 +886,6 @@ else:


 if is_flax_available():
-    from .models.auto import FLAX_MODEL_MAPPING, FlaxAutoModel
    from .models.bert import FlaxBertModel
    from .models.roberta import FlaxRobertaModel
 else:
@@ -19,7 +19,7 @@
 import copy
 import json
 import os
-from typing import Any, Dict, Tuple, Union
+from typing import Any, Dict, Tuple

 from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
 from .utils import logging
@@ -55,6 +55,8 @@ class PretrainedConfig(object):
            Whether or not the model should return all hidden-states.
        output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not the model should returns all attentions.
+        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
+            Whether or not the model should return the last key/values attentions (not used by all models).
        return_dict (:obj:`bool`, `optional`, defaults to :obj:`True`):
            Whether or not the model should return a :class:`~transformers.file_utils.ModelOutput` instead of a plain
            tuple.
@@ -166,6 +168,7 @@ class PretrainedConfig(object):
        self.return_dict = kwargs.pop("return_dict", True)
        self.output_hidden_states = kwargs.pop("output_hidden_states", False)
        self.output_attentions = kwargs.pop("output_attentions", False)
+        self.use_cache = kwargs.pop("use_cache", True)  # Not used by all models
        self.torchscript = kwargs.pop("torchscript", False)  # Only used by PyTorch models
        self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
        self.pruned_heads = kwargs.pop("pruned_heads", {})
@@ -262,13 +265,13 @@ class PretrainedConfig(object):
        self.id2label = {i: "LABEL_{}".format(i) for i in range(num_labels)}
        self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))

-    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
+    def save_pretrained(self, save_directory: str):
        """
        Save a configuration object to the directory ``save_directory``, so that it can be re-loaded using the
        :func:`~transformers.PretrainedConfig.from_pretrained` class method.

        Args:
-            save_directory (:obj:`str` or :obj:`os.PathLike`):
+            save_directory (:obj:`str`):
                Directory where the configuration JSON file will be saved (will be created if it does not exist).
        """
        if os.path.isfile(save_directory):
@@ -281,13 +284,13 @@ class PretrainedConfig(object):
        logger.info("Configuration saved in {}".format(output_config_file))

    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+    def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "PretrainedConfig":
        r"""
        Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pretrained model
        configuration.

        Args:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path (:obj:`str`):
                This can be either:

                - a string, the `model id` of a pretrained model configuration hosted inside a model repo on
@@ -297,7 +300,7 @@ class PretrainedConfig(object):
                  :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g., ``./my_model_directory/``.
                - a path or url to a saved configuration JSON `file`, e.g.,
                  ``./my_model_directory/configuration.json``.
-            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
+            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -346,15 +349,13 @@ class PretrainedConfig(object):
        return cls.from_dict(config_dict, **kwargs)

    @classmethod
-    def get_config_dict(
-        cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
-    ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
+    def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]:
        """
        From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a
        :class:`~transformers.PretrainedConfig` using ``from_dict``.

        Parameters:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path (:obj:`str`):
                The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.

        Returns:
@@ -368,7 +369,6 @@ class PretrainedConfig(object):
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)

-        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
        if os.path.isdir(pretrained_model_name_or_path):
            config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
        elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
@@ -454,12 +454,12 @@ class PretrainedConfig(object):
            return config

    @classmethod
-    def from_json_file(cls, json_file: Union[str, os.PathLike]) -> "PretrainedConfig":
+    def from_json_file(cls, json_file: str) -> "PretrainedConfig":
        """
        Instantiates a :class:`~transformers.PretrainedConfig` from the path to a JSON file of parameters.

        Args:
-            json_file (:obj:`str` or :obj:`os.PathLike`):
+            json_file (:obj:`str`):
                Path to the JSON file containing the parameters.

        Returns:
@@ -470,7 +470,7 @@ class PretrainedConfig(object):
        return cls(**config_dict)

    @classmethod
-    def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
+    def _dict_from_json_file(cls, json_file: str):
        with open(json_file, "r", encoding="utf-8") as reader:
            text = reader.read()
        return json.loads(text)
@@ -540,12 +540,12 @@ class PretrainedConfig(object):
            config_dict = self.to_dict()
        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"

-    def to_json_file(self, json_file_path: Union[str, os.PathLike], use_diff: bool = True):
+    def to_json_file(self, json_file_path: str, use_diff: bool = True):
        """
        Save this instance to a JSON file.

        Args:
-            json_file_path (:obj:`str` or :obj:`os.PathLike`):
+            json_file_path (:obj:`str`):
                Path to the JSON file in which this configuration instance's parameters will be saved.
            use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
                If set to ``True``, only the difference between the config instance and the default
@@ -302,7 +302,7 @@ def convert_pt_checkpoint_to_tf(
        pytorch_checkpoint_url = hf_bucket_url(pytorch_checkpoint_path, filename=WEIGHTS_NAME)
        pytorch_checkpoint_path = cached_path(pytorch_checkpoint_url, force_download=not use_cached_models)
    # Load PyTorch checkpoint in tf2 model:
-    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, config)
+    tf_model = load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path)

    if compare_with_pt_model:
        tfo = tf_model(tf_model.dummy_inputs, training=False)  # build the network
@@ -382,22 +382,6 @@ class AlbertConverter(SpmConverter):
        )


-class BarthezConverter(SpmConverter):
-    def unk_id(self, proto):
-        unk_id = 3
-        return unk_id
-
-    def post_processor(self):
-        return processors.TemplateProcessing(
-            single="<s> $A </s>",
-            pair="<s> $A </s> </s> $B </s>",
-            special_tokens=[
-                ("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")),
-                ("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
-            ],
-        )
-
-
 class CamembertConverter(SpmConverter):
    def vocab(self, proto):
        vocab = [
@@ -547,12 +531,10 @@ class BertGenerationConverter(SpmConverter):
 class PegasusConverter(SpmConverter):
    def vocab(self, proto):
        vocab = [
-            (self.original_tokenizer.pad_token, 0.0),
-            (self.original_tokenizer.eos_token, 0.0),
-            (self.original_tokenizer.mask_token_sent, 0.0),
-            (self.original_tokenizer.mask_token, 0.0),
+            (self.original_tokenizer.pad_token, 0),
+            (self.original_tokenizer.eos_token, 0),
        ]
-        vocab += [(f"<unk_{i}>", -100.0) for i in range(2, self.original_tokenizer.offset)]
+        vocab += [(f"unk_{i}", -100) for i in range(2, 2 + self.original_tokenizer.offset)]
        vocab += [(piece.piece, piece.score) for piece in proto.pieces[2:]]
        return vocab

@@ -561,10 +543,13 @@ class PegasusConverter(SpmConverter):

    def post_processor(self):
        eos = self.original_tokenizer.eos_token
-        special_tokens = [
-            (eos, self.original_tokenizer.eos_token_id),
-        ]
-        return processors.TemplateProcessing(single=["$A", eos], pair=["$A", "$B", eos], special_tokens=special_tokens)
+        return processors.TemplateProcessing(
+            single=["$A", eos],
+            pair=["$A", "$B", eos],
+            special_tokens=[
+                (eos, self.original_tokenizer.eos_token_id),
+            ],
+        )


 class T5Converter(SpmConverter):
@@ -587,7 +572,6 @@ class T5Converter(SpmConverter):
 SLOW_TO_FAST_CONVERTERS = {
    "AlbertTokenizer": AlbertConverter,
    "BartTokenizer": RobertaConverter,
-    "BarthezTokenizer": BarthezConverter,
    "BertTokenizer": BertConverter,
    "CamembertTokenizer": CamembertConverter,
    "DistilBertTokenizer": BertConverter,
@@ -46,7 +46,7 @@ def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path,

        add_prefix = True
        if checkpoint_name is None:
-            checkpoint_names = list(tokenizer_class.max_model_input_sizes.keys())
+            raise ValueError("Please supply a checkpoint name")
        else:
            checkpoint_names = [checkpoint_name]

@@ -78,16 +78,16 @@ def convert_slow_checkpoint_to_fast(tokenizer_name, checkpoint_name, dump_path,
                "=> {} with prefix {}, add_prefix {}".format(dump_path_full, checkpoint_prefix_name, add_prefix)
            )

-            if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
-                file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
-                next_char = file_path.split(checkpoint)[-1][0]
-                if next_char == "/":
-                    dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
-                    checkpoint_prefix_name = None
+            # if checkpoint in list(tokenizer.pretrained_vocab_files_map.values())[0]:
+            #     file_path = list(tokenizer.pretrained_vocab_files_map.values())[0][checkpoint]
+            #     next_char = file_path.split(checkpoint)[-1][0]
+            #     if next_char == "/":
+            #         dump_path_full = os.path.join(dump_path_full, checkpoint_prefix_name)
+            #         checkpoint_prefix_name = None

-                logger.info(
-                    "=> {} with prefix {}, add_prefix {}".format(dump_path_full, checkpoint_prefix_name, add_prefix)
-                )
+            #     logger.info(
+            #         "=> {} with prefix {}, add_prefix {}".format(dump_path_full, checkpoint_prefix_name, add_prefix)
+            #     )

            file_names = tokenizer.save_pretrained(
                dump_path_full, legacy_format=False, filename_prefix=checkpoint_prefix_name
@@ -20,14 +20,14 @@ DataCollator = NewType("DataCollator", Callable[[List[InputDataClass]], Dict[str

 def default_data_collator(features: List[InputDataClass]) -> Dict[str, torch.Tensor]:
    """
-    Very simple data collator that simply collates batches of dict-like objects and performs special handling for
+    Very simple data collator that simply collates batches of dict-like objects and erforms special handling for
    potential keys named:

        - ``label``: handles a single value (int or float) per object
        - ``label_ids``: handles a list of values per object

-    Does not do any additional preprocessing: property names of the input object will be used as corresponding inputs
-    to the model. See glue and ner for example of how it's useful.
+    Des not do any additional preprocessing: property names of the input object will be used as corresponding inputs to
+    the model. See glue and ner for example of how it's useful.
    """

    # In this function we'll make the assumption that all `features` in the batch
@@ -229,7 +229,7 @@ class LineByLineWithSOPTextDataset(Dataset):
        # to `block_size` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
-        # sequences to minimize the mismatch between pretraining and fine-tuning.
+        # sequences to minimize the mismatch between pre-training and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `block_size` is a hard limit.
        target_seq_length = max_num_tokens
@@ -425,7 +425,7 @@ class TextDatasetForNextSentencePrediction(Dataset):
        # to `block_size` anyways, so short sequences are generally wasted
        # computation. However, we *sometimes*
        # (i.e., short_seq_prob == 0.1 == 10% of the time) want to use shorter
-        # sequences to minimize the mismatch between pretraining and fine-tuning.
+        # sequences to minimize the mismatch between pre-training and fine-tuning.
        # The `target_seq_length` is just a rough target however, whereas
        # `block_size` is a hard limit.
        target_seq_length = max_num_tokens
@@ -1,28 +0,0 @@
-import sys
-
-from .dependency_versions_table import deps
-from .utils.versions import require_version_core
-
-
-# define which module versions we always want to check at run time
-# (usually the ones defined in `install_requires` in setup.py)
-#
-# order specific notes:
-# - tqdm must be checked before tokenizers
-
-pkgs_to_check_at_runtime = "python tqdm regex sacremoses requests packaging filelock numpy tokenizers".split()
-if sys.version_info < (3, 7):
-    pkgs_to_check_at_runtime.append("dataclasses")
-
-for pkg in pkgs_to_check_at_runtime:
-    if pkg in deps:
-        if pkg == "tokenizers":
-            # must be loaded here, or else tqdm check may fail
-            from .file_utils import is_tokenizers_available
-
-            if not is_tokenizers_available():
-                continue  # not required, check version only if installed
-
-        require_version_core(deps[pkg])
-    else:
-        raise ValueError(f"can't find {pkg} in {deps.keys()}, check dependency_versions_table.py")
@@ -1,52 +0,0 @@
-# THIS FILE HAS BEEN AUTOGENERATED. To update:
-# 1. modify the `_deps` dict in setup.py
-# 2. run `make deps_table_update``
-deps = {
-    "black": "black>=20.8b1",
-    "cookiecutter": "cookiecutter==1.7.2",
-    "dataclasses": "dataclasses",
-    "datasets": "datasets",
-    "faiss-cpu": "faiss-cpu",
-    "fastapi": "fastapi",
-    "filelock": "filelock",
-    "flake8": "flake8>=3.8.3",
-    "flax": "flax==0.2.2",
-    "fugashi": "fugashi>=1.0",
-    "ipadic": "ipadic>=1.0.0,<2.0",
-    "isort": "isort>=5.5.4",
-    "jax": "jax>=0.2.0",
-    "jaxlib": "jaxlib==0.1.55",
-    "keras2onnx": "keras2onnx",
-    "numpy": "numpy",
-    "onnxconverter-common": "onnxconverter-common",
-    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
-    "onnxruntime": "onnxruntime>=1.4.0",
-    "packaging": "packaging",
-    "parameterized": "parameterized",
-    "protobuf": "protobuf",
-    "psutil": "psutil",
-    "pydantic": "pydantic",
-    "pytest": "pytest",
-    "pytest-xdist": "pytest-xdist",
-    "python": "python>=3.6.0",
-    "recommonmark": "recommonmark",
-    "regex": "regex!=2019.12.17",
-    "requests": "requests",
-    "sacremoses": "sacremoses",
-    "scikit-learn": "scikit-learn",
-    "sentencepiece": "sentencepiece==0.1.91",
-    "sphinx-copybutton": "sphinx-copybutton",
-    "sphinx-markdown-tables": "sphinx-markdown-tables",
-    "sphinx-rtd-theme": "sphinx-rtd-theme==0.4.3",
-    "sphinx": "sphinx==3.2.1",
-    "starlette": "starlette",
-    "tensorflow-cpu": "tensorflow-cpu>=2.0",
-    "tensorflow": "tensorflow>=2.0",
-    "timeout-decorator": "timeout-decorator",
-    "tokenizers": "tokenizers==0.9.4",
-    "torch": "torch>=1.0",
-    "tqdm": "tqdm>=4.27",
-    "unidic": "unidic>=1.0.2",
-    "unidic_lite": "unidic_lite>=1.0.7",
-    "uvicorn": "uvicorn",
-}
@@ -234,6 +234,7 @@ WEIGHTS_NAME = "pytorch_model.bin"
 TF2_WEIGHTS_NAME = "tf_model.h5"
 TF_WEIGHTS_NAME = "model.ckpt"
 CONFIG_NAME = "config.json"
+TOKENIZER_CONFIG_NAME = "tokenizer_config.json"
 MODEL_CARD_NAME = "modelcard.json"

 SENTENCEPIECE_UNDERLINE = "▁"
@@ -34,11 +34,10 @@ class TFGenerationMixin:
        Implement in subclasses of :class:`~transformers.TFPreTrainedModel` for custom behavior to prepare inputs in
        the generate method.
        """
-        return {"input_ids": inputs}
+        return {"inputs": inputs}

    def _use_cache(self, outputs, use_cache):
        """During generation, decide whether to pass the `past` variable to the next forward pass."""
-        use_cache = getattr(self.config, "use_cache", False)
        if len(outputs) <= 1 or use_cache is False:
            return False
        if hasattr(self.config, "mem_len") and self.config.mem_len == 0:
@@ -195,6 +194,7 @@ class TFGenerationMixin:
        min_length = min_length if min_length is not None else self.config.min_length
        do_sample = do_sample if do_sample is not None else self.config.do_sample
        early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
        num_beams = num_beams if num_beams is not None else self.config.num_beams
        temperature = temperature if temperature is not None else self.config.temperature
        top_k = top_k if top_k is not None else self.config.top_k
@@ -224,6 +224,7 @@ class TFGenerationMixin:
        assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer."
        assert isinstance(do_sample, bool), "`do_sample` should be a boolean."
        assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean."
+        assert isinstance(use_cache, bool), "`use_cache` should be a boolean."
        assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer."
        assert temperature > 0, "`temperature` should be strictly positive."
        assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer."
@@ -462,6 +462,7 @@ class GenerationMixin:
        pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id
        bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id
        eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id
+        use_cache = use_cache if use_cache is not None else self.config.use_cache

        if input_ids is None:
            # init `input_ids` with bos_token_id
@@ -43,13 +43,6 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="")
          other
    """
    tf_name = tf_name.replace(":0", "")  # device ids
-
-    # support for ConvBERT model
-    tf_name_split = tf_name.split("/")
-    if tf_name_split[-1] == "depthwise_kernel":
-        tf_name = "/".join(tf_name_split[:-1]) + "/depthwise/weight"
-    if tf_name_split[-1] == "pointwise_kernel":
-        tf_name = "/".join(tf_name_split[:-1]) + "/pointwise/weight"
    tf_name = re.sub(
        r"/[^/]*___([^/]*)/", r"/\1/", tf_name
    )  # '$1___$2' is replaced by $2 (can be used to duplicate or remove layers in TF2.0 vs PyTorch)
@@ -73,6 +66,7 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="")
    tf_name = ".".join(tf_name)
    if start_prefix_to_remove:
        tf_name = tf_name.replace(start_prefix_to_remove, "", 1)
+
    return tf_name, transpose


@@ -81,9 +75,7 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="")
 #####################


-def load_pytorch_checkpoint_in_tf2_model(
-    tf_model, pytorch_checkpoint_path, config, tf_inputs=None, allow_missing_keys=False
-):
+def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False):
    """Load pytorch checkpoints in a TF 2.0 model"""
    try:
        import tensorflow as tf  # noqa: F401
@@ -102,7 +94,7 @@ def load_pytorch_checkpoint_in_tf2_model(
    logger.info("PyTorch checkpoint contains {:,} parameters".format(sum(t.numel() for t in pt_state_dict.values())))

    return load_pytorch_weights_in_tf2_model(
-        tf_model, pt_state_dict, config=config, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys
+        tf_model, pt_state_dict, tf_inputs=tf_inputs, allow_missing_keys=allow_missing_keys
    )


@@ -115,7 +107,7 @@ def load_pytorch_model_in_tf2_model(tf_model, pt_model, tf_inputs=None, allow_mi
    )


-def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, config=None, tf_inputs=None, allow_missing_keys=False):
+def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False):
    """Load pytorch state_dict in a TF 2.0 model."""
    try:
        import tensorflow as tf  # noqa: F401
@@ -172,34 +164,15 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, config=None, tf_i
            if allow_missing_keys:
                missing_keys.append(name)
                continue
-            elif tf_model._keys_to_ignore_on_load_missing is not None:
+            elif tf_model.authorized_missing_keys is not None:
                # authorized missing keys don't have to be loaded
-                if any(re.search(pat, name) is not None for pat in tf_model._keys_to_ignore_on_load_missing):
+                if any(re.search(pat, name) is not None for pat in tf_model.authorized_missing_keys):
                    continue

            raise AttributeError("{} not found in PyTorch model".format(name))

        array = pt_state_dict[name].numpy()

-        if config.model_type == "conv_bert":
-            if name.endswith("depthwise.weight"):
-                array = numpy.transpose(array, axes=(2, 0, 1))
-                transpose = False
-
-            if name.endswith("pointwise.weight"):
-                array = numpy.transpose(array, axes=(2, 1, 0))
-                transpose = False
-
-            if name.endswith("conv_attn_key.bias"):
-                array = numpy.squeeze(array)
-                transpose = False
-
-            if config.num_groups > 1:
-                if name.endswith("intermediate.dense.weight") or name.endswith("output.dense.weight"):
-                    if not name.endswith("attention.output.dense.weight"):
-                        print(name)
-                        transpose = False
-
        if transpose:
            array = numpy.transpose(array)

@@ -236,11 +209,11 @@ def load_pytorch_weights_in_tf2_model(tf_model, pt_state_dict, config=None, tf_i

    unexpected_keys = list(all_pytorch_weights)

-    if tf_model._keys_to_ignore_on_load_missing is not None:
-        for pat in tf_model._keys_to_ignore_on_load_missing:
+    if tf_model.authorized_missing_keys is not None:
+        for pat in tf_model.authorized_missing_keys:
            missing_keys = [k for k in missing_keys if re.search(pat, k) is None]
-    if tf_model._keys_to_ignore_on_load_unexpected is not None:
-        for pat in tf_model._keys_to_ignore_on_load_unexpected:
+    if tf_model.authorized_unexpected_keys is not None:
+        for pat in tf_model.authorized_unexpected_keys:
            unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

    if len(unexpected_keys) > 0:
@@ -14,9 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """TF general model utils."""
-
 import functools
-import inspect
 import os
 import re
 import warnings
@@ -29,17 +27,8 @@ from tensorflow.python.keras import backend as K
 from tensorflow.python.keras.saving import hdf5_format

 from .configuration_utils import PretrainedConfig
-from .file_utils import (
-    DUMMY_INPUTS,
-    TF2_WEIGHTS_NAME,
-    WEIGHTS_NAME,
-    ModelOutput,
-    cached_path,
-    hf_bucket_url,
-    is_remote_url,
-)
+from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
 from .generation_tf_utils import TFGenerationMixin
-from .tokenization_utils_base import BatchEncoding
 from .utils import logging


@@ -247,110 +236,6 @@ class TFNextSentencePredictionLoss:
        return loss_fn(next_sentence_label, next_sentence_reduced_logits)


-def input_processing(func, input_ids, **kwargs):
-    signature = dict(inspect.signature(func).parameters)
-    signature.pop("kwargs", None)
-    parameter_names = list(signature.keys())
-    output = {}
-    allowed_types = (tf.Tensor, bool, int, ModelOutput, tuple, list, dict)
-
-    if "inputs" in kwargs["kwargs_call"]:
-        warnings.warn(
-            "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.",
-            FutureWarning,
-        )
-
-        output["input_ids"] = kwargs["kwargs_call"].pop("inputs")
-
-    if "decoder_cached_states" in kwargs["kwargs_call"]:
-        warnings.warn(
-            "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
-            FutureWarning,
-        )
-        output["past_key_values"] = kwargs["kwargs_call"].pop("decoder_cached_states")
-
-    if len(kwargs["kwargs_call"]) > 0:
-        raise ValueError(
-            f"The following keyword arguments are not supported by this model: {list(kwargs['kwargs_call'].keys())}."
-        )
-
-    for k, v in kwargs.items():
-        if isinstance(v, allowed_types) or v is None:
-            output[k] = v
-        else:
-            raise ValueError(f"Data of type {type(v)} is not allowed only tf.Tensor is accepted for {k}.")
-
-    if isinstance(input_ids, (tuple, list)):
-        for i, input in enumerate(input_ids):
-            # EagerTensors don't allow to use the .name property so we check for a real Tensor
-            if type(input) == tf.Tensor:
-                # Tensor names have always the pattern name:device_id then we check only the
-                # name and not the device id
-                tensor_name = input.name.split(":")[0]
-
-                if tensor_name in parameter_names:
-                    output[tensor_name] = input
-                else:
-                    raise ValueError(
-                        f"The tensor named {input.name} does not belong to the authorized list of names {parameter_names}."
-                    )
-            elif isinstance(input, allowed_types) or input is None:
-                output[parameter_names[i]] = input
-            else:
-                raise ValueError(
-                    f"Data of type {type(input)} is not allowed only tf.Tensor is accepted for {parameter_names[i]}."
-                )
-    elif isinstance(input_ids, (dict, BatchEncoding)):
-        if "inputs" in input_ids:
-            warnings.warn(
-                "The `inputs` argument is deprecated and will be removed in a future version, use `input_ids` instead.",
-                FutureWarning,
-            )
-
-            output["input_ids"] = input_ids.pop("inputs")
-
-        if "decoder_cached_states" in input_ids:
-            warnings.warn(
-                "The `decoder_cached_states` argument is deprecated and will be removed in a future version, use `past_key_values` instead.",
-                FutureWarning,
-            )
-            output["past_key_values"] = input_ids.pop("decoder_cached_states")
-
-        for k, v in dict(input_ids).items():
-            if not isinstance(v, allowed_types):
-                raise ValueError(f"Data of type {type(v)} is not allowed only tf.Tensor is accepted for {k}.")
-            else:
-                output[k] = v
-    else:
-        if isinstance(input_ids, tf.Tensor) or input_ids is None:
-            output[parameter_names[0]] = input_ids
-        else:
-            raise ValueError(
-                f"Data of type {type(input_ids)} is not allowed only tf.Tensor is accepted for {parameter_names[0]}."
-            )
-
-    for name in parameter_names:
-        if name not in list(output.keys()) and name != "args":
-            output[name] = kwargs.pop(name, signature[name].default)
-
-    # When creating a SavedModel TF calls the method with LayerCall.__call__(args, **kwargs)
-    # So to respect the proper output we have to add this exception
-    if "args" in output:
-        if output["args"] is not None and type(output["args"]) == tf.Tensor:
-            tensor_name = output["args"].name.split(":")[0]
-            output[tensor_name] = output["args"]
-        else:
-            # `args` in this case is always the first parameter, then `input_ids`
-            output["input_ids"] = output["args"]
-
-        del output["args"]
-
-    if "kwargs" in output:
-        del output["kwargs"]
-
-    return output
-
-
 def load_tf_weights(model, resolved_archive_file):
    """
    Detect missing and unexpected layers and load the TF weights accordingly to their names and shapes.
@@ -458,15 +343,15 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
          :class:`~transformers.PretrainedConfig` to use as configuration class for this model architecture.
        - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
          derived classes of the same architecture adding modules on top of the base model.
+        - **authorized_missing_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to ignore
+          from the model when loading the model weights (and avoid unnecessary warnings).
+        - **authorized_unexpected_keys** (:obj:`List[str]`, `optional`) -- A list of re pattern of tensor names to
+          ignore from the weights when loading the model weights (and avoid unnecessary warnings).
    """
    config_class = None
    base_model_prefix = ""
-    # a list of re pattern of tensor names to ignore from the model when loading the model weights
-    # (and avoid unnecessary warnings).
-    _keys_to_ignore_on_load_missing = None
-    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
-    # (and avoid unnecessary warnings).
-    _keys_to_ignore_on_load_unexpected = None
+    authorized_missing_keys = None
+    authorized_unexpected_keys = None

    @property
    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
@@ -500,7 +385,6 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
            :obj:`tf.keras.layers.Layer`: A torch module mapping vocabulary to hidden states.
        """
        base_model = getattr(self, self.base_model_prefix, self)
-
        if base_model is not self:
            return base_model.get_input_embeddings()
        else:
@@ -841,7 +725,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
            from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model

            # Load from a PyTorch checkpoint
-            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, config, allow_missing_keys=True)
+            return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file, allow_missing_keys=True)

        model(model.dummy_inputs, training=False)  # build the network with dummy inputs

@@ -858,12 +742,12 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):

        model(model.dummy_inputs, training=False)  # Make sure restore ops are run

-        if cls._keys_to_ignore_on_load_missing is not None:
-            for pat in cls._keys_to_ignore_on_load_missing:
+        if cls.authorized_missing_keys is not None:
+            for pat in cls.authorized_missing_keys:
                missing_keys = [k for k in missing_keys if re.search(pat, k) is None]

-        if cls._keys_to_ignore_on_load_unexpected is not None:
-            for pat in cls._keys_to_ignore_on_load_unexpected:
+        if cls.authorized_unexpected_keys is not None:
+            for pat in cls.authorized_unexpected_keys:
                unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

        if len(unexpected_keys) > 0:
@@ -1163,13 +1047,8 @@ def shape_list(tensor: tf.Tensor) -> List[int]:
    Returns:
        :obj:`List[int]`: The shape of the tensor as a list.
    """
-    dynamic = tf.shape(tensor)
-
-    if tensor.shape == tf.TensorShape(None):
-        return dynamic.as_list()
-
    static = tensor.shape.as_list()
-
+    dynamic = tf.shape(tensor)
    return [dynamic[i] if s is None else s for i, s in enumerate(static)]


@@ -404,18 +404,17 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):

        - **base_model_prefix** (:obj:`str`) -- A string indicating the attribute associated to the base model in
          derived classes of the same architecture adding modules on top of the base model.
+        - **authorized_missing_keys** (:obj:`Optional[List[str]]`) -- A list of re pattern of tensor names to ignore
+          when loading the model (and avoid unnecessary warnings).
+        - **keys_to_never_save** (:obj:`Optional[List[str]]`) -- A list of of tensor names to ignore when saving the
+          model (useful for keys that aren't trained, but which are deterministic)
+
    """
    config_class = None
    base_model_prefix = ""
-    # a list of re pattern of tensor names to ignore from the model when loading the model weights
-    # (and avoid unnecessary warnings).
-    _keys_to_ignore_on_load_missing = None
-    # a list of re pattern of tensor names to ignore from the weights when loading the model weights
-    # (and avoid unnecessary warnings).
-    _keys_to_ignore_on_load_unexpected = None
-    # a list of of tensor names to ignore when saving the model (useful for keys that aren't
-    # trained, but which are deterministic)
-    _keys_to_ignore_on_save = None
+    authorized_missing_keys = None
+    authorized_unexpected_keys = None
+    keys_to_never_save = None

    @property
    def dummy_inputs(self) -> Dict[str, torch.Tensor]:
@@ -697,13 +696,13 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):

        self.base_model._prune_heads(heads_to_prune)

-    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
+    def save_pretrained(self, save_directory):
        """
        Save a model and its configuration file to a directory, so that it can be re-loaded using the
        `:func:`~transformers.PreTrainedModel.from_pretrained`` class method.

        Arguments:
-            save_directory (:obj:`str` or :obj:`os.PathLike`):
+            save_directory (:obj:`str`):
                Directory to which to save. Will be created if it doesn't exist.
        """
        if os.path.isfile(save_directory):
@@ -720,8 +719,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
        state_dict = model_to_save.state_dict()

        # Handle the case where some state_dict keys shouldn't be saved
-        if self._keys_to_ignore_on_save is not None:
-            state_dict = {k: v for k, v in state_dict.items() if k not in self._keys_to_ignore_on_save}
+        if self.keys_to_never_save is not None:
+            state_dict = {k: v for k, v in state_dict.items() if k not in self.keys_to_never_save}

        # If we save using the predefined names, we can load using `from_pretrained`
        output_model_file = os.path.join(save_directory, WEIGHTS_NAME)
@@ -741,7 +740,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
        logger.info("Model weights saved in {}".format(output_model_file))

    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
        r"""
        Instantiate a pretrained pytorch model from a pre-trained model configuration.

@@ -756,7 +755,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
        weights are discarded.

        Parameters:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`, `optional`):
+            pretrained_model_name_or_path (:obj:`str`, `optional`):
                Can be either:

                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
@@ -772,11 +771,11 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                      arguments ``config`` and ``state_dict``).
            model_args (sequence of positional arguments, `optional`):
                All remaning positional arguments will be passed to the underlying model's ``__init__`` method.
-            config (:obj:`Union[PretrainedConfig, str, os.PathLike]`, `optional`):
+            config (:obj:`Union[PretrainedConfig, str]`, `optional`):
                Can be either:

                    - an instance of a class derived from :class:`~transformers.PretrainedConfig`,
-                    - a string or path valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.
+                    - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained`.

                Configuration for the model to use instead of an automatically loaded configuation. Configuration can
                be automatically loaded when:
@@ -794,7 +793,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
                weights. In this case though, you should check if using
                :func:`~transformers.PreTrainedModel.save_pretrained` and
                :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
-            cache_dir (:obj:`Union[str, os.PathLike]`, `optional`):
+            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -881,7 +880,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):

        # Load model
        if pretrained_model_name_or_path is not None:
-            pretrained_model_name_or_path = str(pretrained_model_name_or_path)
            if os.path.isdir(pretrained_model_name_or_path):
                if from_tf and os.path.isfile(os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")):
                    # Load from a TF 1.0 checkpoint in priority if from_tf
@@ -1036,12 +1034,12 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):

            # Some models may have keys that are not in the state by design, removing them before needlessly warning
            # the user.
-            if cls._keys_to_ignore_on_load_missing is not None:
-                for pat in cls._keys_to_ignore_on_load_missing:
+            if cls.authorized_missing_keys is not None:
+                for pat in cls.authorized_missing_keys:
                    missing_keys = [k for k in missing_keys if re.search(pat, k) is None]

-            if cls._keys_to_ignore_on_load_unexpected is not None:
-                for pat in cls._keys_to_ignore_on_load_unexpected:
+            if cls.authorized_unexpected_keys is not None:
+                for pat in cls.authorized_unexpected_keys:
                    unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None]

            if len(unexpected_keys) > 0:
@@ -7,10 +7,10 @@ from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertCo


 if is_sentencepiece_available():
-    from .tokenization_albert import AlbertTokenizer
+    from .tokenization_albert import AlbertTokenizer, ALBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST

 if is_tokenizers_available():
-    from .tokenization_albert_fast import AlbertTokenizerFast
+    from .tokenization_albert_fast import AlbertTokenizerFast, ALBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST

 if is_torch_available():
    from .modeling_albert import (
@@ -78,13 +78,6 @@ class AlbertConfig(PretrainedConfig):
            The epsilon used by the layer normalization layers.
        classifier_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
            The dropout ratio for attached classifiers.
-        position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
-            Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
-            :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
-            :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
-            <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
-            `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
-            <https://arxiv.org/abs/2009.13658>`__.

    Examples::

@@ -126,7 +119,6 @@ class AlbertConfig(PretrainedConfig):
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        classifier_dropout_prob=0.1,
-        position_embedding_type="absolute",
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=3,
@@ -150,4 +142,3 @@ class AlbertConfig(PretrainedConfig):
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.classifier_dropout_prob = classifier_dropout_prob
-        self.position_embedding_type = position_embedding_type
@@ -214,7 +214,6 @@ class AlbertEmbeddings(nn.Module):

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")

    # Copied from transformers.models.bert.modeling_bert.BertEmbeddings.forward
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
@@ -233,12 +232,10 @@ class AlbertEmbeddings(nn.Module):

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
@@ -268,11 +265,6 @@ class AlbertAttention(nn.Module):
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pruned_heads = set()

-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)
-
    # Copied from transformers.models.bert.modeling_bert.BertSelfAttention.transpose_for_scores
    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
@@ -297,10 +289,10 @@ class AlbertAttention(nn.Module):
        self.all_head_size = self.attention_head_size * self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)

-    def forward(self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False):
-        mixed_query_layer = self.query(hidden_states)
-        mixed_key_layer = self.key(hidden_states)
-        mixed_value_layer = self.value(hidden_states)
+    def forward(self, input_ids, attention_mask=None, head_mask=None, output_attentions=False):
+        mixed_query_layer = self.query(input_ids)
+        mixed_key_layer = self.key(input_ids)
+        mixed_value_layer = self.value(input_ids)

        query_layer = self.transpose_for_scores(mixed_query_layer)
        key_layer = self.transpose_for_scores(mixed_key_layer)
@@ -309,27 +301,10 @@ class AlbertAttention(nn.Module):
        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
-
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
            attention_scores = attention_scores + attention_mask

-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
        # Normalize the attention scores to probabilities.
        attention_probs = nn.Softmax(dim=-1)(attention_scores)

@@ -355,7 +330,7 @@ class AlbertAttention(nn.Module):

        projected_context_layer = torch.einsum("bfnd,ndh->bfh", context_layer, w) + b
        projected_context_layer_dropout = self.output_dropout(projected_context_layer)
-        layernormed_context_layer = self.LayerNorm(hidden_states + projected_context_layer_dropout)
+        layernormed_context_layer = self.LayerNorm(input_ids + projected_context_layer_dropout)
        return (layernormed_context_layer, attention_probs) if output_attentions else (layernormed_context_layer,)


@@ -484,7 +459,7 @@ class AlbertPreTrainedModel(PreTrainedModel):

    config_class = AlbertConfig
    base_model_prefix = "albert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
+    authorized_missing_keys = [r"position_ids"]

    def _init_weights(self, module):
        """Initialize the weights."""
@@ -730,7 +705,7 @@ class AlbertModel(AlbertPreTrainedModel):

@add_start_docstrings(
    """
-    Albert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
+    Albert Model with two heads on top as done during the pre-training: a `masked language modeling` head and a
    `sentence order prediction (classification)` head.
    """,
    ALBERT_START_DOCSTRING,
@@ -876,7 +851,7 @@ class AlbertSOPHead(nn.Module):
 )
 class AlbertForMaskedLM(AlbertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
@@ -1046,7 +1021,7 @@ class AlbertForSequenceClassification(AlbertPreTrainedModel):
 )
 class AlbertForTokenClassification(AlbertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
@@ -1135,7 +1110,7 @@ class AlbertForTokenClassification(AlbertPreTrainedModel):
 )
 class AlbertForQuestionAnswering(AlbertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
@@ -47,10 +47,10 @@ from ...modeling_tf_utils import (
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
-    input_processing,
    keras_serializable,
    shape_list,
 )
+from ...tokenization_utils import BatchEncoding
 from ...utils import logging
 from .configuration_albert import AlbertConfig

@@ -516,7 +516,7 @@ class TFAlbertMainLayer(tf.keras.layers.Layer):

    def call(
        self,
-        input_ids=None,
+        inputs,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -526,52 +526,56 @@ class TFAlbertMainLayer(tf.keras.layers.Layer):
        output_hidden_states=None,
        return_dict=None,
        training=False,
-        **kwargs,
    ):
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-            kwargs_call=kwargs,
-        )
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
+            position_ids = inputs[3] if len(inputs) > 3 else position_ids
+            head_mask = inputs[4] if len(inputs) > 4 else head_mask
+            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            return_dict = inputs[8] if len(inputs) > 8 else return_dict
+            assert len(inputs) <= 9, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            token_type_ids = inputs.get("token_type_ids", token_type_ids)
+            position_ids = inputs.get("position_ids", position_ids)
+            head_mask = inputs.get("head_mask", head_mask)
+            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            return_dict = inputs.get("return_dict", return_dict)
+            assert len(inputs) <= 9, "Too many inputs."
+        else:
+            input_ids = inputs

-        output_attentions = (
-            inputs["output_attentions"] if inputs["output_attentions"] is not None else self.output_attentions
-        )
-        output_hidden_states = (
-            inputs["output_hidden_states"] if inputs["output_hidden_states"] is not None else self.output_hidden_states
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.return_dict
+        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.return_dict

-        if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
+        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif inputs["input_ids"] is not None:
-            input_shape = shape_list(inputs["input_ids"])
-        elif inputs["inputs_embeds"] is not None:
-            input_shape = shape_list(inputs["inputs_embeds"])[:-1]
+        elif input_ids is not None:
+            input_shape = shape_list(input_ids)
+        elif inputs_embeds is not None:
+            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

-        if inputs["attention_mask"] is None:
-            inputs["attention_mask"] = tf.fill(input_shape, 1)
-
-        if inputs["token_type_ids"] is None:
-            inputs["token_type_ids"] = tf.fill(input_shape, 0)
+        if attention_mask is None:
+            attention_mask = tf.fill(input_shape, 1)
+        if token_type_ids is None:
+            token_type_ids = tf.fill(input_shape, 0)

        # We create a 3D attention mask from a 2D tensor mask.
        # Sizes are [batch_size, 1, 1, to_seq_length]
        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
        # this attention mask is more simple than the triangular masking of causal attention
        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = inputs["attention_mask"][:, tf.newaxis, tf.newaxis, :]
+        extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
@@ -587,26 +591,21 @@ class TFAlbertMainLayer(tf.keras.layers.Layer):
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        if inputs["head_mask"] is not None:
+        if head_mask is not None:
            raise NotImplementedError
        else:
-            inputs["head_mask"] = [None] * self.num_hidden_layers
+            head_mask = [None] * self.num_hidden_layers
+            # head_mask = tf.constant([0] * self.num_hidden_layers)

-        embedding_output = self.embeddings(
-            inputs["input_ids"],
-            inputs["position_ids"],
-            inputs["token_type_ids"],
-            inputs["inputs_embeds"],
-            training=inputs["training"],
-        )
+        embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
        encoder_outputs = self.encoder(
            embedding_output,
            extended_attention_mask,
-            inputs["head_mask"],
+            head_mask,
            output_attentions,
            output_hidden_states,
            return_dict,
-            training=inputs["training"],
+            training=training,
        )

        sequence_output = encoder_outputs[0]
@@ -762,54 +761,14 @@ class TFAlbertModel(TFAlbertPreTrainedModel):
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
    )
-    def call(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        training=False,
-        **kwargs,
-    ):
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-            kwargs_call=kwargs,
-        )
-
-        outputs = self.albert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=inputs["return_dict"],
-            training=inputs["training"],
-        )
-
+    def call(self, inputs, **kwargs):
+        outputs = self.albert(inputs, **kwargs)
        return outputs


@add_start_docstrings(
    """
-    Albert Model with two heads on top for pretraining: a `masked language modeling` head and a `sentence order
+    Albert Model with two heads on top for pre-training: a `masked language modeling` head and a `sentence order
    prediction` (classification) head.
    """,
    ALBERT_START_DOCSTRING,
@@ -828,20 +787,7 @@ class TFAlbertForPreTraining(TFAlbertPreTrainedModel):

    @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @replace_return_docstrings(output_type=TFAlbertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    def call(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        training=False,
-        **kwargs,
-    ):
+    def call(self, inputs, **kwargs):
        r"""
        Return:

@@ -859,38 +805,12 @@ class TFAlbertForPreTraining(TFAlbertPreTrainedModel):
            >>> prediction_logits = outputs.prediction_logits
            >>> sop_logits = outputs.sop_logits
        """
-
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-            kwargs_call=kwargs,
-        )
-
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.albert.return_dict
-        outputs = self.albert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
-        )
+        return_dict = kwargs.get("return_dict")
+        return_dict = return_dict if return_dict is not None else self.albert.return_dict
+        outputs = self.albert(inputs, **kwargs)
        sequence_output, pooled_output = outputs[:2]
        prediction_scores = self.predictions(sequence_output)
-        sop_scores = self.sop_classifier(pooled_output, training=inputs["training"])
+        sop_scores = self.sop_classifier(pooled_output, training=kwargs.get("training", False))

        if not return_dict:
            return (prediction_scores, sop_scores) + outputs[2:]
@@ -923,7 +843,7 @@ class TFAlbertSOPHead(tf.keras.layers.Layer):
@add_start_docstrings("""Albert Model with a `language modeling` head on top. """, ALBERT_START_DOCSTRING)
 class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):

-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -943,7 +863,7 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -954,7 +874,6 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -962,9 +881,16 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.albert.return_dict
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.albert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -973,27 +899,13 @@ class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss)
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
        )

-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.albert.return_dict
-        outputs = self.albert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
-        )
        sequence_output = outputs[0]
-        prediction_scores = self.predictions(sequence_output, training=inputs["training"])
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores)
+        prediction_scores = self.predictions(sequence_output, training=training)
+
+        loss = None if labels is None else self.compute_loss(labels, prediction_scores)

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
@@ -1034,7 +946,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1045,7 +957,6 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1053,9 +964,16 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
            config.num_labels - 1]``. If ``config.num_labels == 1`` a regression loss is computed (Mean-Square loss),
            If ``config.num_labels > 1`` a classification loss is computed (Cross-Entropy).
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.albert.return_dict
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.albert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1064,27 +982,15 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.albert.return_dict
-        outputs = self.albert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
+
        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output, training=inputs["training"])
+
+        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
+
+        loss = None if labels is None else self.compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[2:]
@@ -1107,7 +1013,7 @@ class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClass
 )
 class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss):

-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -1128,7 +1034,7 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1139,16 +1045,22 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
            1]``.
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.albert.return_dict
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.albert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1157,27 +1069,15 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.albert.return_dict
-        outputs = self.albert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
+
        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output, training=inputs["training"])
+
+        sequence_output = self.dropout(sequence_output, training=training)
        logits = self.classifier(sequence_output)
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
+
+        loss = None if labels is None else self.compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[2:]
@@ -1200,7 +1100,7 @@ class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificat
 )
 class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss):

-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -1220,7 +1120,7 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1232,7 +1132,6 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
        start_positions=None,
        end_positions=None,
        training=False,
-        **kwargs,
    ):
        r"""
        start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1244,9 +1143,18 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
            sequence are not taken into account for computing the loss.
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.albert.return_dict
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[9] if len(inputs) > 9 else start_positions
+            end_positions = inputs[10] if len(inputs) > 10 else end_positions
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
+        outputs = self.albert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1255,34 +1163,20 @@ class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringL
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            start_positions=start_positions,
-            end_positions=end_positions,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.albert.return_dict
-        outputs = self.albert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
+
        sequence_output = outputs[0]
+
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = tf.split(logits, 2, axis=-1)
        start_logits = tf.squeeze(start_logits, axis=-1)
        end_logits = tf.squeeze(end_logits, axis=-1)
-        loss = None

-        if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
-            labels = {"start_position": inputs["start_positions"]}
-            labels["end_position"] = inputs["end_positions"]
+        loss = None
+        if start_positions is not None and end_positions is not None:
+            labels = {"start_position": start_positions}
+            labels["end_position"] = end_positions
            loss = self.compute_loss(labels, (start_logits, end_logits))

        if not return_dict:
@@ -1334,7 +1228,7 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
    )
    def call(
        self,
-        input_ids=None,
+        inputs,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1345,7 +1239,6 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1353,41 +1246,48 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
            num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
            :obj:`input_ids` above)
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            labels=labels,
-            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.albert.return_dict
-
-        if inputs["input_ids"] is not None:
-            num_choices = shape_list(inputs["input_ids"])[1]
-            seq_length = shape_list(inputs["input_ids"])[2]
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
+            position_ids = inputs[3] if len(inputs) > 3 else position_ids
+            head_mask = inputs[4] if len(inputs) > 4 else head_mask
+            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            return_dict = inputs[8] if len(inputs) > 8 else return_dict
+            labels = inputs[9] if len(inputs) > 9 else labels
+            assert len(inputs) <= 10, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            token_type_ids = inputs.get("token_type_ids", token_type_ids)
+            position_ids = inputs.get("position_ids", position_ids)
+            head_mask = inputs.get("head_mask", head_mask)
+            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            return_dict = inputs.get("return_dict", return_dict)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 10, "Too many inputs."
        else:
-            num_choices = shape_list(inputs["inputs_embeds"])[1]
-            seq_length = shape_list(inputs["inputs_embeds"])[2]
+            input_ids = inputs
+        return_dict = return_dict if return_dict is not None else self.albert.return_dict

-        flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
-        flat_attention_mask = (
-            tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
-        )
-        flat_token_type_ids = (
-            tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
-        )
+        if input_ids is not None:
+            num_choices = shape_list(input_ids)[1]
+            seq_length = shape_list(input_ids)[2]
+        else:
+            num_choices = shape_list(inputs_embeds)[1]
+            seq_length = shape_list(inputs_embeds)[2]
+
+        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
        flat_inputs_embeds = (
-            tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
-            if inputs["inputs_embeds"] is not None
+            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+            if inputs_embeds is not None
            else None
        )

@@ -1396,21 +1296,21 @@ class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss):
            flat_attention_mask,
            flat_token_type_ids,
            flat_position_ids,
-            inputs["head_mask"],
+            head_mask,
            flat_inputs_embeds,
-            inputs["output_attentions"],
-            inputs["output_hidden_states"],
+            output_attentions,
+            output_hidden_states,
            return_dict=return_dict,
-            training=inputs["training"],
+            training=training,
        )

        pooled_output = outputs[1]

-        pooled_output = self.dropout(pooled_output, training=inputs["training"])
+        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))

-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)
+        loss = None if labels is None else self.compute_loss(labels, reshaped_logits)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
@@ -27,31 +27,20 @@ from ...utils import logging


 logger = logging.get_logger(__name__)
+
 VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
-        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
-        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
-        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
-        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
-        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
-        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
-        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
-    }
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "albert-base-v1": 512,
-    "albert-large-v1": 512,
-    "albert-xlarge-v1": 512,
-    "albert-xxlarge-v1": 512,
-    "albert-base-v2": 512,
-    "albert-large-v2": 512,
-    "albert-xlarge-v2": 512,
-    "albert-xxlarge-v2": 512,
-}
+ALBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
+    "albert-base-v1",
+    "albert-large-v1",
+    "albert-xlarge-v1",
+    "albert-xxlarge-v1",
+    "albert-base-v2",
+    "albert-large-v2",
+    "albert-xlarge-v2",
+    "albert-xxlarge-v2",
+    # See all ALBERT models at https://huggingface.co/models?filter=albert
+]

 SPIECE_UNDERLINE = "▁"

@@ -108,8 +97,7 @@ class AlbertTokenizer(PreTrainedTokenizer):
    """

    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    max_model_input_sizes = 512

    def __init__(
        self,
@@ -32,49 +32,27 @@ else:
 logger = logging.get_logger(__name__)
 VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
-        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
-        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
-        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
-        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
-        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
-        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
-        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
-    },
-    "tokenizer_file": {
-        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
-        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
-        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
-        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
-        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
-        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
-        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
-        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "albert-base-v1": 512,
-    "albert-large-v1": 512,
-    "albert-xlarge-v1": 512,
-    "albert-xxlarge-v1": 512,
-    "albert-base-v2": 512,
-    "albert-large-v2": 512,
-    "albert-xlarge-v2": 512,
-    "albert-xxlarge-v2": 512,
-}
+ALBERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
+    "albert-base-v1",
+    "albert-large-v1",
+    "albert-xlarge-v1",
+    "albert-xxlarge-v1",
+    "albert-base-v2",
+    "albert-large-v2",
+    "albert-xlarge-v2",
+    "albert-xxlarge-v2",
+    # See all ALBERT models at https://huggingface.co/models?filter=albert
+]

 SPIECE_UNDERLINE = "▁"


 class AlbertTokenizerFast(PreTrainedTokenizerFast):
    """
-    Construct a "fast" ALBERT tokenizer (backed by HuggingFace's `tokenizers` library). Based on `Unigram
-    <https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models>`__. This tokenizer
-    inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main methods. Users should
-    refer to this superclass for more information regarding those methods
+    Construct a "fast" ALBERT tokenizer (backed by HuggingFace's `tokenizers` library). Based on `SentencePiece
+    <https://github.com/google/sentencepiece>`__. This tokenizer inherits from
+    :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main methods. Users should refer to this
+    superclass for more information regarding those methods

    Args:
        vocab_file (:obj:`str`):
@@ -113,8 +91,7 @@ class AlbertTokenizerFast(PreTrainedTokenizerFast):
    """

    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    max_model_input_sizes = 512
    slow_tokenizer_class = AlbertTokenizer

    def __init__(
@@ -2,8 +2,8 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.

-from ...file_utils import is_flax_available, is_tf_available, is_torch_available
-from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
+from ...file_utils import is_tf_available, is_torch_available
+from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, AutoConfig
 from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer


@@ -57,6 +57,3 @@ if is_tf_available():
        TFAutoModelForTokenClassification,
        TFAutoModelWithLMHead,
    )
-
-if is_flax_available():
-    from .modeling_flax_auto import FLAX_MODEL_MAPPING, FlaxAutoModel
@@ -24,7 +24,6 @@ from ..bert.configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertCo
 from ..bert_generation.configuration_bert_generation import BertGenerationConfig
 from ..blenderbot.configuration_blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig
 from ..camembert.configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
-from ..conv_bert.configuration_conv_bert import CONV_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig
 from ..ctrl.configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
 from ..deberta.configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
 from ..distilbert.configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
@@ -65,7 +64,6 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
    (key, value)
    for pretrained_map in [
        # Add archive maps here
-        CONV_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BART_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -103,7 +101,6 @@ ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
 CONFIG_MAPPING = OrderedDict(
    [
        # Add configs here
-        ("conv_bert", ConvBertConfig),
        ("retribert", RetriBertConfig),
        ("mt5", MT5Config),
        ("t5", T5Config),
@@ -147,7 +144,6 @@ CONFIG_MAPPING = OrderedDict(
 MODEL_NAMES_MAPPING = OrderedDict(
    [
        # Add full (and cased) model names here
-        ("conv_bert", "ConvBert"),
        ("retribert", "RetriBERT"),
        ("t5", "T5"),
        ("mobilebert", "MobileBERT"),
@@ -278,7 +274,7 @@ class AutoConfig:
        List options

        Args:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path (:obj:`str`):
                Can be either:

                    - A string, the `model id` of a pretrained model configuration hosted inside a model repo on
@@ -289,7 +285,7 @@ class AutoConfig:
                      :meth:`~transformers.PreTrainedModel.save_pretrained` method, e.g., ``./my_model_directory/``.
                    - A path or url to a saved configuration JSON `file`, e.g.,
                      ``./my_model_directory/configuration.json``.
-            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
+            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -350,7 +346,7 @@ class AutoConfig:
        else:
            # Fallback: use pattern matching on the string.
            for pattern, config_class in CONFIG_MAPPING.items():
-                if pattern in str(pretrained_model_name_or_path):
+                if pattern in pretrained_model_name_or_path:
                    return config_class.from_dict(config_dict, **kwargs)

        raise ValueError(
@@ -21,6 +21,8 @@ from collections import OrderedDict
 from ...configuration_utils import PretrainedConfig
 from ...file_utils import add_start_docstrings
 from ...utils import logging
+
+# Add modeling imports here
 from ..albert.modeling_albert import (
    AlbertForMaskedLM,
    AlbertForMultipleChoice,
@@ -58,14 +60,6 @@ from ..camembert.modeling_camembert import (
    CamembertForTokenClassification,
    CamembertModel,
 )
-from ..conv_bert.modeling_conv_bert import (
-    ConvBertForMaskedLM,
-    ConvBertForMultipleChoice,
-    ConvBertForQuestionAnswering,
-    ConvBertForSequenceClassification,
-    ConvBertForTokenClassification,
-    ConvBertModel,
-)
 from ..ctrl.modeling_ctrl import CTRLLMHeadModel, CTRLModel
 from ..deberta.modeling_deberta import DebertaForSequenceClassification, DebertaModel
 from ..distilbert.modeling_distilbert import (
@@ -202,7 +196,6 @@ from .configuration_auto import (
    BertGenerationConfig,
    BlenderbotConfig,
    CamembertConfig,
-    ConvBertConfig,
    CTRLConfig,
    DebertaConfig,
    DistilBertConfig,
@@ -237,17 +230,12 @@ from .configuration_auto import (
 )


-# Add modeling imports here
-
-
-
 logger = logging.get_logger(__name__)


 MODEL_MAPPING = OrderedDict(
    [
        # Base model mapping
-        (ConvBertConfig, ConvBertModel),
        (RetriBertConfig, RetriBertModel),
        (MT5Config, MT5Model),
        (T5Config, T5Model),
@@ -315,7 +303,6 @@ MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
 MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
    [
        # Model with LM heads mapping
-        (ConvBertConfig, ConvBertForMaskedLM),
        (LayoutLMConfig, LayoutLMForMaskedLM),
        (T5Config, T5ForConditionalGeneration),
        (DistilBertConfig, DistilBertForMaskedLM),
@@ -370,7 +357,6 @@ MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
 MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
    [
        # Model for Masked LM mapping
-        (ConvBertConfig, ConvBertForMaskedLM),
        (LayoutLMConfig, LayoutLMForMaskedLM),
        (DistilBertConfig, DistilBertForMaskedLM),
        (AlbertConfig, AlbertForMaskedLM),
@@ -410,7 +396,6 @@ MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING = OrderedDict(
 MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
    [
        # Model for Sequence Classification mapping
-        (ConvBertConfig, ConvBertForSequenceClassification),
        (DistilBertConfig, DistilBertForSequenceClassification),
        (AlbertConfig, AlbertForSequenceClassification),
        (CamembertConfig, CamembertForSequenceClassification),
@@ -436,7 +421,6 @@ MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
    [
        # Model for Question Answering mapping
-        (ConvBertConfig, ConvBertForQuestionAnswering),
        (DistilBertConfig, DistilBertForQuestionAnswering),
        (AlbertConfig, AlbertForQuestionAnswering),
        (CamembertConfig, CamembertForQuestionAnswering),
@@ -460,7 +444,6 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING = OrderedDict(
 MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
    [
        # Model for Token Classification mapping
-        (ConvBertConfig, ConvBertForTokenClassification),
        (LayoutLMConfig, LayoutLMForTokenClassification),
        (DistilBertConfig, DistilBertForTokenClassification),
        (CamembertConfig, CamembertForTokenClassification),
@@ -483,7 +466,6 @@ MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
 MODEL_FOR_MULTIPLE_CHOICE_MAPPING = OrderedDict(
    [
        # Model for Multiple Choice mapping
-        (ConvBertConfig, ConvBertForMultipleChoice),
        (CamembertConfig, CamembertForMultipleChoice),
        (ElectraConfig, ElectraForMultipleChoice),
        (XLMRobertaConfig, XLMRobertaForMultipleChoice),
@@ -520,7 +502,7 @@ AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
        deactivated). To train the model, you should first set it back in training mode with ``model.train()``

        Args:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path:
                Can be either:

                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
@@ -551,7 +533,7 @@ AUTO_MODEL_PRETRAINED_DOCSTRING = r"""
                weights. In this case though, you should check if using
                :func:`~transformers.PreTrainedModel.save_pretrained` and
                :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
-            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
+            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            from_tf (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -36,7 +36,7 @@ ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict(
    for key, value, in pretrained_map.items()
 )

-FLAX_MODEL_MAPPING = OrderedDict(
+MODEL_MAPPING = OrderedDict(
    [
        (RobertaConfig, FlaxRobertaModel),
        (BertConfig, FlaxBertModel),
@@ -79,13 +79,13 @@ class FlaxAutoModel(object):
            model = FlaxAutoModel.from_config(config)
            # E.g. model was saved using `save_pretrained('./test/saved_model/')`
        """
-        for config_class, model_class in FLAX_MODEL_MAPPING.items():
+        for config_class, model_class in MODEL_MAPPING.items():
            if isinstance(config, config_class):
                return model_class(config)
        raise ValueError(
            f"Unrecognized configuration class {config.__class__} "
            f"for this kind of FlaxAutoModel: {cls.__name__}.\n"
-            f"Model type should be one of {', '.join(c.__name__ for c in FLAX_MODEL_MAPPING.keys())}."
+            f"Model type should be one of {', '.join(c.__name__ for c in MODEL_MAPPING.keys())}."
        )

    @classmethod
@@ -173,11 +173,11 @@ class FlaxAutoModel(object):
        if not isinstance(config, PretrainedConfig):
            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)

-        for config_class, model_class in FLAX_MODEL_MAPPING.items():
+        for config_class, model_class in MODEL_MAPPING.items():
            if isinstance(config, config_class):
                return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
        raise ValueError(
            f"Unrecognized configuration class {config.__class__} "
            f"for this kind of FlaxAutoModel: {cls.__name__}.\n"
-            f"Model type should be one of {', '.join(c.__name__ for c in FLAX_MODEL_MAPPING.keys())}"
+            f"Model type should be one of {', '.join(c.__name__ for c in MODEL_MAPPING.keys())}"
        )
@@ -14,11 +14,14 @@
 # limitations under the License.
 """ Auto Tokenizer class. """

+import json
+import os

 from collections import OrderedDict

 from ...configuration_utils import PretrainedConfig
-from ...file_utils import is_sentencepiece_available, is_tokenizers_available
+from ...file_utils import is_sentencepiece_available, is_tokenizers_available, cached_path, hf_bucket_url, is_remote_url
+from ...tokenization_utils_base import TOKENIZER_CONFIG_FILE
 from ...utils import logging
 from ..bart.tokenization_bart import BartTokenizer
 from ..bert.tokenization_bert import BertTokenizer
@@ -72,7 +75,6 @@ from .configuration_auto import (
    MarianConfig,
    MBartConfig,
    MobileBertConfig,
-    MT5Config,
    OpenAIGPTConfig,
    PegasusConfig,
    ProphetNetConfig,
@@ -93,8 +95,8 @@ from .configuration_auto import (

 if is_sentencepiece_available():
    from ..albert.tokenization_albert import AlbertTokenizer
-    from ..barthez.tokenization_barthez import BarthezTokenizer
    from ..bert_generation.tokenization_bert_generation import BertGenerationTokenizer
+    from ..blenderbot.tokenization_blenderbot import BlenderbotTokenizer
    from ..camembert.tokenization_camembert import CamembertTokenizer
    from ..marian.tokenization_marian import MarianTokenizer
    from ..mbart.tokenization_mbart import MBartTokenizer
@@ -106,8 +108,8 @@ if is_sentencepiece_available():
    from ..xlnet.tokenization_xlnet import XLNetTokenizer
 else:
    AlbertTokenizer = None
-    BarthezTokenizer = None
    BertGenerationTokenizer = None
+    BlenderbotTokenizer = None
    CamembertTokenizer = None
    MarianTokenizer = None
    MBartTokenizer = None
@@ -121,7 +123,6 @@ else:
 if is_tokenizers_available():
    from ..albert.tokenization_albert_fast import AlbertTokenizerFast
    from ..bart.tokenization_bart_fast import BartTokenizerFast
-    from ..barthez.tokenization_barthez_fast import BarthezTokenizerFast
    from ..bert.tokenization_bert_fast import BertTokenizerFast
    from ..camembert.tokenization_camembert_fast import CamembertTokenizerFast
    from ..distilbert.tokenization_distilbert_fast import DistilBertTokenizerFast
@@ -147,7 +148,6 @@ if is_tokenizers_available():
 else:
    AlbertTokenizerFast = None
    BartTokenizerFast = None
-    BarthezTokenizerFast = None
    BertTokenizerFast = None
    CamembertTokenizerFast = None
    DistilBertTokenizerFast = None
@@ -178,7 +178,6 @@ TOKENIZER_MAPPING = OrderedDict(
    [
        (RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)),
        (T5Config, (T5Tokenizer, T5TokenizerFast)),
-        (MT5Config, (T5Tokenizer, T5TokenizerFast)),
        (MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)),
        (DistilBertConfig, (DistilBertTokenizer, DistilBertTokenizerFast)),
        (AlbertConfig, (AlbertTokenizer, AlbertTokenizerFast)),
@@ -189,7 +188,6 @@ TOKENIZER_MAPPING = OrderedDict(
        (MarianConfig, (MarianTokenizer, None)),
        (BlenderbotConfig, (BlenderbotSmallTokenizer, None)),
        (LongformerConfig, (LongformerTokenizer, LongformerTokenizerFast)),
-        (BartConfig, (BarthezTokenizer, BarthezTokenizerFast)),
        (BartConfig, (BartTokenizer, BartTokenizerFast)),
        (LongformerConfig, (LongformerTokenizer, LongformerTokenizerFast)),
        (RobertaConfig, (RobertaTokenizer, RobertaTokenizerFast)),
@@ -224,8 +222,11 @@ NO_CONFIG_TOKENIZER = [
    HerbertTokenizer,
    HerbertTokenizerFast,
    PhobertTokenizer,
+    BlenderbotTokenizer,
 ]

+class NoTokenizerClassAttribute(Exception):
+    pass

 SLOW_TOKENIZER_MAPPING = {
    k: (v[0] if v[0] is not None else v[1])
@@ -272,7 +273,7 @@ class AutoTokenizer:
        List options

        Params:
-            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
+            pretrained_model_name_or_path (:obj:`str`):
                Can be either:

                    - A string, the `model id` of a predefined tokenizer hosted inside a model repo on huggingface.co.
@@ -288,7 +289,7 @@ class AutoTokenizer:
                Will be passed along to the Tokenizer ``__init__()`` method.
            config (:class:`~transformers.PreTrainedConfig`, `optional`)
                The configuration object used to dertermine the tokenizer class to instantiate.
-            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
+            cache_dir (:obj:`str`, `optional`):
                Path to a directory in which a downloaded pretrained model configuration should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
@@ -329,18 +330,70 @@ class AutoTokenizer:

        """
        config = kwargs.pop("config", None)
-        if not isinstance(config, PretrainedConfig):
-            config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        tokenizer_class_name = config.tokenizer_class if isinstance(config, PretrainedConfig) else None
+
+        if tokenizer_class_name is None:
+            # Try first to load a tokenizer_config.json file
+            # This logic is identical to the from_pretrained logic in configuration_utils.py
+            # We duplicate it because we don't have (yet?) a full class for tokenizer config, they are just simple dicts
+            cache_dir = kwargs.get("cache_dir", None)
+            force_download = kwargs.get("force_download", False)
+            resume_download = kwargs.get("resume_download", False)
+            proxies = kwargs.get("proxies", None)
+            local_files_only = kwargs.get("local_files_only", False)
+            revision = kwargs.get("revision", None)
+
+            if os.path.isdir(pretrained_model_name_or_path):
+                tokenizer_config_file = os.path.join(pretrained_model_name_or_path, TOKENIZER_CONFIG_FILE)
+            elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
+                tokenizer_config_file = pretrained_model_name_or_path
+            else:
+                tokenizer_config_file = hf_bucket_url(
+                    pretrained_model_name_or_path, filename=TOKENIZER_CONFIG_FILE, revision=revision, mirror=None
+                )
+
+            try:
+                # Load from URL or cache if already cached
+                resolved_tokenizer_config_file = cached_path(
+                    tokenizer_config_file,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    local_files_only=local_files_only,
+                )
+                # Load config dict
+                with open(resolved_tokenizer_config_file, "r", encoding="utf-8") as reader:
+                    text = reader.read()
+                config = json.loads(text)
+
+                tokenizer_class_name = config.get("tokenizer_class_name", None)
+                if tokenizer_class_name is None:
+                    raise NoTokenizerClassAttribute
+
+            # If we cannot find the tokenizer_config file (EnvironmentError)
+            # or cannot decode it (json.JSONDecodeError)
+            # or it doesn't have a "tokenizer_class" attribute (NoTokenizerClassAttribute)
+            # We try to load the configuration for the model
+            except (EnvironmentError, json.JSONDecodeError, NoTokenizerClassAttribute):
+                # Here we are ok to raise the errors because we found no way to identify the tokenizer class
+                model_config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
+                tokenizer_class_name = model_config.tokenizer_class
+
+                # We only overwrite the potentially provided config if it's None
+                # Since it can be used to detect class (see below the type(config) test)
+                if config is None:
+                    config = model_config

        use_fast = kwargs.pop("use_fast", True)

-        if config.tokenizer_class is not None:
+        if tokenizer_class_name is not None:
            tokenizer_class = None
-            if use_fast and not config.tokenizer_class.endswith("Fast"):
-                tokenizer_class_candidate = f"{config.tokenizer_class}Fast"
+            if use_fast and not tokenizer_class_name.endswith("Fast"):
+                tokenizer_class_candidate = f"{tokenizer_class_name}Fast"
                tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)
            if tokenizer_class is None:
-                tokenizer_class_candidate = config.tokenizer_class
+                tokenizer_class_candidate = tokenizer_class_name
                tokenizer_class = tokenizer_class_from_name(tokenizer_class_candidate)

            if tokenizer_class is None:
@@ -349,6 +402,9 @@ class AutoTokenizer:
                )
            return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)

+        # Could find a tokenizer_class in the tokenizer or model config.
+        # Let's try other (backward compatible) options
+
        # if model is an encoder decoder, the encoder tokenizer class is used by default
        if isinstance(config, EncoderDecoderConfig):
            if type(config.decoder) is not type(config.encoder):  # noqa: E721
@@ -4,11 +4,11 @@

 from ...file_utils import is_tf_available, is_tokenizers_available, is_torch_available
 from .configuration_bart import BartConfig
-from .tokenization_bart import BartTokenizer
+from .tokenization_bart import BartTokenizer, BART_PRETRAINED_TOKENIZER_ARCHIVE_LIST


 if is_tokenizers_available():
-    from .tokenization_bart_fast import BartTokenizerFast
+    from .tokenization_bart_fast import BartTokenizerFast, BART_PRETRAINED_TOKENIZER_ARCHIVE_LIST

 if is_torch_available():
    from .modeling_bart import (
@@ -108,8 +108,6 @@ class BartConfig(PretrainedConfig):
        force_bos_token_to_be_generated (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to force BOS token to be generated at step 1 (after ``decoder_start_token_id``), only
            :obj:`True` for `bart-large-cnn`.
-        use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
    """
    model_type = "bart"
    keys_to_ignore_at_inference = ["past_key_values"]
@@ -136,6 +134,9 @@ class BartConfig(PretrainedConfig):
        classifier_dropout=0.0,
        num_labels=3,
        is_encoder_decoder=True,
+        pad_token_id=1,
+        bos_token_id=0,
+        eos_token_id=2,
        normalize_before=False,
        add_final_layer_norm=False,
        do_blenderbot_90_layernorm=False,
@@ -144,10 +145,6 @@ class BartConfig(PretrainedConfig):
        static_position_embeddings=False,
        add_bias_logits=False,
        force_bos_token_to_be_generated=False,
-        use_cache=True,
-        pad_token_id=1,
-        bos_token_id=0,
-        eos_token_id=2,
        **common_kwargs
    ):
        r"""
@@ -211,8 +208,6 @@ class BartConfig(PretrainedConfig):

        self.do_blenderbot_90_layernorm = do_blenderbot_90_layernorm

-        self.use_cache = use_cache
-
    @property
    def num_attention_heads(self) -> int:
        return self.encoder_attention_heads
@@ -358,13 +358,11 @@ class BartEncoder(nn.Module):
        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

-        encoder_states = () if output_hidden_states else None
+        encoder_states = [] if output_hidden_states else None
        all_attentions = () if output_attentions else None
        for encoder_layer in self.layers:
            if output_hidden_states:
-                x = x.transpose(0, 1)  # T x B x C -> B x T x C
-                encoder_states = encoder_states + (x,)
-                x = x.transpose(0, 1)  # B x T x C -> T x B x C
+                encoder_states.append(x)
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):  # skip the layer
@@ -377,13 +375,14 @@ class BartEncoder(nn.Module):

        if self.layer_norm:
            x = self.layer_norm(x)
+        if output_hidden_states:
+            encoder_states.append(x)
+            # T x B x C -> B x T x C
+            encoder_states = tuple(hidden_state.transpose(0, 1) for hidden_state in encoder_states)

        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

-        if output_hidden_states:
-            encoder_states = encoder_states + (x,)
-
        if not return_dict:
            return tuple(v for v in [x, encoder_states, all_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=x, hidden_states=encoder_states, attentions=all_attentions)
@@ -584,9 +583,7 @@ class BartDecoder(nn.Module):
        for idx, decoder_layer in enumerate(self.layers):
            # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
            if output_hidden_states:
-                x = x.transpose(0, 1)
                all_hidden_states += (x,)
-                x = x.transpose(0, 1)
            dropout_probability = random.uniform(0, 1)
            if self.training and (dropout_probability < self.layerdrop):
                continue
@@ -610,16 +607,12 @@ class BartDecoder(nn.Module):
                all_self_attns += (layer_self_attn,)
                all_cross_attentions += (layer_cross_attn,)

-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            x = x.transpose(0, 1)
-            all_hidden_states += (x,)
-            x = x.transpose(0, 1)
-
        if self.layer_norm:  # if config.add_final_layer_norm (mBART)
            x = self.layer_norm(x)

        # Convert to standard output format: (seq_len, BS, model_dim) -> (BS, seq_len, model_dim)
+        if output_hidden_states:
+            all_hidden_states = tuple(hidden_state.transpose(0, 1) for hidden_state in all_hidden_states)
        x = x.transpose(0, 1)
        encoder_hidden_states = encoder_hidden_states.transpose(0, 1)

@@ -735,16 +728,7 @@ class Attention(nn.Module):
            reshaped = key_padding_mask.unsqueeze(1).unsqueeze(2)
            attn_weights = attn_weights.masked_fill(reshaped, float("-inf"))
            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
-
        attn_weights = F.softmax(attn_weights, dim=-1)
-
-        if output_attentions:
-            # make sure that attn_weights are included in graph
-            attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
-            attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
-        else:
-            attn_weights_reshaped = None
-
        attn_probs = F.dropout(attn_weights, p=self.dropout, training=self.training)

        assert v is not None
@@ -752,8 +736,11 @@ class Attention(nn.Module):
        assert attn_output.size() == (bsz * self.num_heads, tgt_len, self.head_dim)
        attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
        attn_output = self.out_proj(attn_output)
-
-        return attn_output, attn_weights_reshaped
+        if output_attentions:
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+        else:
+            attn_weights = None
+        return attn_output, attn_weights

    def _concat_saved_state(self, k, v, saved_state, static_kv, bsz) -> Tuple[Tensor]:
        # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
@@ -959,7 +946,7 @@ class BartModel(PretrainedBartModel):
 )
 class BartForConditionalGeneration(PretrainedBartModel):
    base_model_prefix = "model"
-    _keys_to_ignore_on_load_missing = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"]
+    authorized_missing_keys = [r"final_logits_bias", r"encoder\.version", r"decoder\.version"]

    def __init__(self, config: BartConfig):
        super().__init__(config)
@@ -16,18 +16,16 @@

 import math
 import random
-from typing import Dict, Optional, Tuple, Union
+import warnings
+from typing import Dict, Optional, Tuple

 import numpy as np
 import tensorflow as tf
+from tensorflow import Tensor
+from tensorflow.keras.layers import Dense, Layer, LayerNormalization

 from ...activations_tf import ACT2FN
-from ...file_utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    replace_return_docstrings,
-)
+from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
 from ...modeling_tf_outputs import (
    TFBaseModelOutput,
    TFBaseModelOutputWithPast,
@@ -42,16 +40,15 @@ from ...modeling_tf_utils import (
    TFSharedEmbeddings,
    TFWrappedEmbeddings,
    cast_bool_to_primitive,
-    input_processing,
    keras_serializable,
    shape_list,
 )
+from ...tokenization_utils_base import BatchEncoding
 from ...utils import logging
 from .configuration_bart import BartConfig


 _CONFIG_FOR_DOC = "BartConfig"
-_TOKENIZER_FOR_DOC = "BartTokenizer"

 BART_START_DOCSTRING = r"""

@@ -226,7 +223,7 @@ PAST_KV_DEPRECATION_WARNING = (
 )


-class TFEncoderLayer(tf.keras.layers.Layer):
+class TFEncoderLayer(Layer):
    def __init__(self, config: BartConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
@@ -234,13 +231,13 @@ class TFEncoderLayer(tf.keras.layers.Layer):
            self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn"
        )
        self.normalize_before = config.normalize_before
-        self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+        self.self_attn_layer_norm = LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
        self.dropout = config.dropout
        self.activation_fn = ACT2FN[config.activation_function]
        self.activation_dropout = config.activation_dropout
-        self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
-        self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
-        self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.fc1 = Dense(config.encoder_ffn_dim, name="fc1")
+        self.fc2 = Dense(self.embed_dim, name="fc2")
+        self.final_layer_norm = LayerNormalization(epsilon=1e-5, name="final_layer_norm")

    def call(self, x, encoder_padding_mask, training=False):
        """
@@ -280,7 +277,7 @@ class TFEncoderLayer(tf.keras.layers.Layer):
        return x, self_attn_weights


-class TFBartEncoder(tf.keras.layers.Layer):
+class TFBartEncoder(Layer):
    # config_class = BartConfig
    """
    Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
@@ -319,15 +316,9 @@ class TFBartEncoder(tf.keras.layers.Layer):
            )
        self.layers = [TFEncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)]
        self.layernorm_embedding = (
-            tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
-            if config.normalize_embedding
-            else tf.keras.layers.Layer()
-        )
-        self.layer_norm = (
-            tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
-            if config.add_final_layer_norm
-            else None
+            LayerNormalization(epsilon=1e-5, name="layernorm_embedding") if config.normalize_embedding else Layer()
        )
+        self.layer_norm = LayerNormalization(epsilon=1e-5, name="layer_norm") if config.add_final_layer_norm else None
        self.return_dict = config.return_dict

    def call(
@@ -350,9 +341,9 @@ class TFBartEncoder(tf.keras.layers.Layer):

                - **x** (Tensor): the last encoder layer's output of shape `(src_len, batch, embed_dim)`

-                - **encoder_states** (List[tf.Tensor]): all intermediate hidden states of shape `(src_len, batch,
+                - **encoder_states** (List[Tensor]): all intermediate hidden states of shape `(src_len, batch,
                  embed_dim)`. Only populated if *output_hidden_states* is True.
-                - **all_attentions** (List[tf.Tensor]): Attention weights for each layer.
+                - **all_attentions** (List[Tensor]): Attention weights for each layer.
                During training might not be of length n_layers because of layer dropout.
        """
        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
@@ -403,7 +394,7 @@ class TFBartEncoder(tf.keras.layers.Layer):
        return TFBaseModelOutput(last_hidden_state=x, hidden_states=encoder_states, attentions=all_attentions)


-class TFDecoderLayer(tf.keras.layers.Layer):
+class TFDecoderLayer(Layer):
    def __init__(self, config: BartConfig, **kwargs):
        super().__init__(**kwargs)
        self.embed_dim = config.d_model
@@ -418,7 +409,7 @@ class TFDecoderLayer(tf.keras.layers.Layer):
        self.activation_dropout = config.activation_dropout
        self.normalize_before = config.normalize_before

-        self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
+        self.self_attn_layer_norm = LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm")
        self.encoder_attn = TFAttention(
            self.embed_dim,
            config.decoder_attention_heads,
@@ -426,10 +417,10 @@ class TFDecoderLayer(tf.keras.layers.Layer):
            encoder_decoder_attention=True,
            name="encoder_attn",
        )
-        self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
-        self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
-        self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
-        self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.encoder_attn_layer_norm = LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm")
+        self.fc1 = Dense(config.decoder_ffn_dim, name="fc1")
+        self.fc2 = Dense(self.embed_dim, name="fc2")
+        self.final_layer_norm = LayerNormalization(epsilon=1e-5, name="final_layer_norm")

    def call(
        self,
@@ -503,7 +494,7 @@ class TFDecoderLayer(tf.keras.layers.Layer):
        )  # just self_attn weights for now, following t5, layer_state = cache for decoding


-class TFBartDecoder(tf.keras.layers.Layer):
+class TFBartDecoder(Layer):
    """
    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a :class:`TFDecoderLayer`

@@ -535,15 +526,9 @@ class TFBartDecoder(tf.keras.layers.Layer):
            )
        self.layers = [TFDecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)]
        self.layernorm_embedding = (
-            tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding")
-            if config.normalize_embedding
-            else tf.keras.layers.Layer()
-        )
-        self.layer_norm = (
-            tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layer_norm")
-            if config.add_final_layer_norm
-            else None
+            LayerNormalization(epsilon=1e-5, name="layernorm_embedding") if config.normalize_embedding else Layer()
        )
+        self.layer_norm = LayerNormalization(epsilon=1e-5, name="layer_norm") if config.add_final_layer_norm else None

        self.dropout = config.dropout
        self.output_hidden_states = config.output_hidden_states
@@ -658,7 +643,7 @@ def _reorder_buffer(attn_cache, new_order):
    return attn_cache


-class TFAttention(tf.keras.layers.Layer):
+class TFAttention(Layer):
    """Multi-headed attention from "Attention Is All You Need"""

    def __init__(
@@ -681,10 +666,10 @@ class TFAttention(tf.keras.layers.Layer):

        self.encoder_decoder_attention = encoder_decoder_attention

-        self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj")
-        self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj")
-        self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj")
-        self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj")
+        self.k_proj = Dense(embed_dim, use_bias=bias, name="k_proj")
+        self.q_proj = Dense(embed_dim, use_bias=bias, name="q_proj")
+        self.v_proj = Dense(embed_dim, use_bias=bias, name="v_proj")
+        self.out_proj = Dense(embed_dim, use_bias=bias, name="out_proj")

        self.cache_key = "encoder_decoder" if self.encoder_decoder_attention else "self"

@@ -698,9 +683,9 @@ class TFAttention(tf.keras.layers.Layer):
        key: tf.Tensor,
        key_padding_mask: Optional[tf.Tensor] = None,
        layer_state: Optional[Dict[str, tf.Tensor]] = None,
-        attn_mask: Optional[tf.Tensor] = None,
+        attn_mask: Optional[Tensor] = None,
        training=False,
-    ) -> Tuple[tf.Tensor, Optional[tf.Tensor]]:
+    ) -> Tuple[Tensor, Optional[Tensor]]:
        """
        Input shape: Time(SeqLen) x Batch x Channel

@@ -914,20 +899,15 @@ class TFBartModel(TFPretrainedBartModel):
        causal_lm_mask = causal_attention_mask(tgt_len, tgt_len, mask_dtype)
        return decoder_input_ids, decoder_padding_mask, causal_lm_mask

-    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
-    @add_code_sample_docstrings(
-        tokenizer_class=_TOKENIZER_FOR_DOC,
-        checkpoint="facebook/bart-large",
-        output_type=TFSeq2SeqModelOutput,
-        config_class=_CONFIG_FOR_DOC,
-    )
+    @add_start_docstrings_to_model_forward(BART_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFSeq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
-        input_ids,
+        inputs,
        attention_mask=None,
        decoder_input_ids=None,  # BAD DEFAULT LEFT FOR CONSISTENT SIGNATURE
        decoder_attention_mask=None,
-        encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None,
+        encoder_outputs: Optional[TFBaseModelOutput] = None,
        past_key_values=None,
        use_cache=None,
        output_attentions=None,
@@ -936,89 +916,93 @@ class TFBartModel(TFPretrainedBartModel):
        training=False,
        **kwargs
    ):
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            decoder_input_ids=decoder_input_ids,
-            decoder_attention_mask=decoder_attention_mask,
-            encoder_outputs=encoder_outputs,
-            past_key_values=past_key_values,
+        """
+        Returns:
+        """
+        assert "decoder_cached_states" not in kwargs, "Please use past_key_values to cache intermediate outputs"
+        if isinstance(inputs, (tuple, list)):
+            assert len(inputs) <= 10, "Too many inputs."
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            decoder_input_ids = inputs[2] if len(inputs) > 2 else decoder_input_ids
+            decoder_attention_mask = inputs[3] if len(inputs) > 3 else decoder_attention_mask
+            encoder_outputs = inputs[4] if len(inputs) > 4 else encoder_outputs
+            past_key_values = inputs[5] if len(inputs) > 5 else past_key_values
+            use_cache = inputs[6] if len(inputs) > 6 else use_cache
+            output_attentions = inputs[7] if len(inputs) > 7 else output_attentions
+            output_hidden_states = inputs[8] if len(inputs) > 8 else output_hidden_states
+            return_dict = inputs[9] if len(inputs) > 9 else return_dict
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            assert len(inputs) <= 10, "Too many inputs."
+            if "inputs" in inputs:
+                raise ValueError("Using `inputs` as a keyword argument is deprecated. Please use `input_ids` instead.")
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            decoder_input_ids = inputs.get("decoder_input_ids", decoder_input_ids)
+            decoder_attention_mask = inputs.get("decoder_attention_mask", decoder_attention_mask)
+            encoder_outputs = inputs.get("encoder_outputs", encoder_outputs)
+            past_key_values = inputs.get("past_key_values", past_key_values)
+            use_cache = inputs.get("use_cache", use_cache)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+        else:
+            input_ids = inputs
+
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if decoder_input_ids is None:  # Classification
+            use_cache = False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        if not use_cache:
+            decoder_input_ids, decoder_padding_mask, causal_mask = self._prepare_bart_decoder_inputs(
+                inputs,
+                decoder_input_ids=decoder_input_ids,
+                decoder_attn_mask=decoder_attention_mask,
+                mask_dtype=self.shared.dtype,
+            )
+        else:
+            decoder_padding_mask, causal_mask = None, None
+        assert (
+            isinstance(encoder_outputs, TFBaseModelOutput) or encoder_outputs is None
+        ), f"got unexpected encoder outputs type {type(encoder_outputs)}"
+        if encoder_outputs is None:
+            encoder_outputs = self.encoder(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=True,
+                training=training,
+            )
+        decoder_outputs = self.decoder(
+            decoder_input_ids,
+            encoder_outputs.last_hidden_state,
+            attention_mask,
+            decoder_padding_mask,
+            decoder_causal_mask=causal_mask,
+            decoder_cached_states=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
-            kwargs_call=kwargs,
        )
-        use_cache = inputs["use_cache"] if inputs["use_cache"] is not None else self.config.use_cache
-        if inputs["decoder_input_ids"] is None:  # Classification
-            use_cache = False
-        output_attentions = (
-            inputs["output_attentions"] if inputs["output_attentions"] is not None else self.config.output_attentions
-        )
-        output_hidden_states = (
-            inputs["output_hidden_states"]
-            if inputs["output_hidden_states"] is not None
-            else self.config.output_hidden_states
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.config.return_dict
-        if not use_cache:
-            inputs["decoder_input_ids"], decoder_padding_mask, causal_mask = self._prepare_bart_decoder_inputs(
-                inputs["input_ids"],
-                decoder_input_ids=inputs["decoder_input_ids"],
-                decoder_attn_mask=inputs["decoder_attention_mask"],
-                mask_dtype=self.shared.dtype,
-            )
-        else:
-            decoder_padding_mask, causal_mask = None, None
-
-        if inputs["encoder_outputs"] is None:
-            inputs["encoder_outputs"] = self.encoder(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
-                training=inputs["training"],
-            )
-        # If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True
-        elif return_dict and not isinstance(inputs["encoder_outputs"], TFBaseModelOutput):
-            inputs["encoder_outputs"] = TFBaseModelOutput(
-                last_hidden_state=inputs["encoder_outputs"][0],
-                hidden_states=inputs["encoder_outputs"][1] if len(inputs["encoder_outputs"]) > 1 else None,
-                attentions=inputs["encoder_outputs"][2] if len(inputs["encoder_outputs"]) > 2 else None,
-            )
-        # If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False
-        elif not return_dict and not isinstance(inputs["encoder_outputs"], tuple):
-            inputs["encoder_outputs"] = inputs["encoder_outputs"].to_tuple()
-
-        decoder_outputs = self.decoder(
-            inputs["decoder_input_ids"],
-            inputs["encoder_outputs"][0],
-            inputs["attention_mask"],
-            decoder_padding_mask,
-            decoder_causal_mask=causal_mask,
-            decoder_cached_states=inputs["past_key_values"],
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=inputs["training"],
-        )
-
        if not return_dict:
-            return decoder_outputs + inputs["encoder_outputs"]
-
-        return TFSeq2SeqModelOutput(
-            last_hidden_state=decoder_outputs.last_hidden_state,
-            past_key_values=decoder_outputs.past_key_values,
-            decoder_hidden_states=decoder_outputs.hidden_states,
-            decoder_attentions=decoder_outputs.attentions,
-            encoder_last_hidden_state=inputs["encoder_outputs"].last_hidden_state,
-            encoder_hidden_states=inputs["encoder_outputs"].hidden_states,
-            encoder_attentions=inputs["encoder_outputs"].attentions,
-        )
+            # Attention and hidden_states will be [] or None if they aren't needed
+            return tuple(x for x in decoder_outputs + encoder_outputs.to_tuple() if x is not None)
+        else:
+            return TFSeq2SeqModelOutput(
+                last_hidden_state=decoder_outputs.last_hidden_state,
+                past_key_values=decoder_outputs.past_key_values,
+                decoder_hidden_states=decoder_outputs.hidden_states,
+                decoder_attentions=decoder_outputs.attentions,
+                encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+                encoder_hidden_states=encoder_outputs.hidden_states,
+                encoder_attentions=encoder_outputs.attentions,
+            )

    def get_input_embeddings(self):
        return self.shared
@@ -1036,16 +1020,16 @@ class TFBartModel(TFPretrainedBartModel):
 )
 class TFBartForConditionalGeneration(TFPretrainedBartModel):
    base_model_prefix = "model"
-    _keys_to_ignore_on_load_missing = [
+    authorized_missing_keys = [
        r"final_logits_bias",
    ]
-    _keys_to_ignore_on_load_unexpected = [
+    authorized_unexpected_keys = [
        r"model.encoder.embed_tokens.weight",
        r"model.decoder.embed_tokens.weight",
    ]

-    def __init__(self, config, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
+    def __init__(self, config: BartConfig, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
        self.model = TFBartModel(config, name="model")
        self.use_cache = config.use_cache
        # final_bias_logits is registered as a buffer in pytorch, so not trainable for the the sake of consistency.
@@ -1057,17 +1041,17 @@ class TFBartForConditionalGeneration(TFPretrainedBartModel):
    @replace_return_docstrings(output_type=TFSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
-        input_ids,
+        inputs,
        attention_mask=None,
        decoder_input_ids=None,
        decoder_attention_mask=None,
        encoder_outputs: Optional[TFBaseModelOutput] = None,
        past_key_values=None,
+        labels=None,
        use_cache=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
-        labels=None,
        training=False,
        **kwargs,
    ):
@@ -1088,59 +1072,87 @@ class TFBartForConditionalGeneration(TFPretrainedBartModel):
            probs = tf.nn.softmax(logits[0])
            # probs[5] is associated with the mask token
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            decoder_input_ids = inputs[2] if len(inputs) > 2 else decoder_input_ids
+            decoder_attention_mask = inputs[3] if len(inputs) > 3 else decoder_attention_mask
+            encoder_outputs = inputs[4] if len(inputs) > 4 else encoder_outputs
+            past_key_values = inputs[5] if len(inputs) > 5 else past_key_values
+            labels = inputs[6] if len(inputs) > 6 else labels
+            use_cache = inputs[7] if len(inputs) > 7 else use_cache
+            output_attentions = inputs[8] if len(inputs) > 8 else output_attentions
+            output_hidden_states = inputs[9] if len(inputs) > 9 else output_hidden_states
+            return_dict = inputs[10] if len(inputs) > 10 else return_dict
+            assert len(inputs) <= 13, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            if "inputs" in inputs:
+                warnings.warn("Using `inputs` as a keyword argument is deprecated. Please use `input_ids` instead.")
+            if "past_key_value_states" in inputs:
+                raise ValueError(PAST_KV_DEPRECATION_WARNING)
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            decoder_input_ids = inputs.get("decoder_input_ids", decoder_input_ids)
+            decoder_attention_mask = inputs.get("decoder_attention_mask", decoder_attention_mask)
+            encoder_outputs = inputs.get("encoder_outputs", encoder_outputs)
+            past_key_values = inputs.get("past_key_values", past_key_values)
+            labels = inputs.get("labels", labels)
+            use_cache = inputs.get("use_cache", use_cache)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            assert len(inputs) <= 13, "Too many inputs."
+
+        else:
+            input_ids = inputs
+        if "past_key_value_states" in kwargs:
+            raise ValueError(PAST_KV_DEPRECATION_WARNING)
+
+        output_attentions = output_attentions if output_attentions else self.config.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states else self.config.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        if labels is not None:
+            use_cache = False
+        outputs: TFSeq2SeqModelOutput = self.model(
+            input_ids,
            attention_mask=attention_mask,
            decoder_input_ids=decoder_input_ids,
-            decoder_attention_mask=decoder_attention_mask,
            encoder_outputs=encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            labels=labels,
-            training=training,
-            kwargs_call=kwargs,
+            return_dict=True,  # TODO(SS): this may need to change to support compilation
        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.config.return_dict
-        use_cache = inputs["use_cache"] if inputs["use_cache"] is not None else self.config.use_cache
-        if inputs["labels"] is not None:
-            use_cache = False
-            if inputs["decoder_input_ids"] is None:
-                inputs["decoder_input_ids"] = self._shift_right(inputs["labels"])
+        logits = self.model.shared(outputs.last_hidden_state, mode="linear")
+        logits = logits + self.final_logits_bias
+        loss = None if labels is None else self.compute_loss(labels, logits)

-        outputs = self.model(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            decoder_input_ids=inputs["decoder_input_ids"],
-            encoder_outputs=inputs["encoder_outputs"],
-            decoder_attention_mask=inputs["decoder_attention_mask"],
-            past_key_values=inputs["past_key_values"],
-            use_cache=use_cache,
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-        )
-        lm_logits = self.model.shared(outputs[0], mode="linear")
-        lm_logits = lm_logits + self.final_logits_bias
-        masked_lm_loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], lm_logits)
+        past = outputs.past_key_values if cast_bool_to_primitive(use_cache, self.config.use_cache) else None

-        if not return_dict:
-            output = (lm_logits,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return TFSeq2SeqLMOutput(
-            loss=masked_lm_loss,
-            logits=lm_logits,
-            past_key_values=outputs.past_key_values,  # index 1 of d outputs
-            decoder_hidden_states=outputs.decoder_hidden_states,  # index 2 of d outputs
-            decoder_attentions=outputs.decoder_attentions,  # index 3 of d outputs
-            encoder_last_hidden_state=outputs.last_hidden_state,  # index 0 of encoder outputs
-            encoder_hidden_states=outputs.encoder_hidden_states,  # 1 of e out
-            encoder_attentions=outputs.encoder_attentions,  # 2 of e out
-        )
+        if return_dict:
+            return TFSeq2SeqLMOutput(
+                loss=loss,
+                logits=logits,
+                past_key_values=past,  # index 1 of d outputs
+                decoder_hidden_states=outputs.decoder_hidden_states,  # index 2 of d outputs
+                decoder_attentions=outputs.decoder_attentions,  # index 3 of d outputs
+                encoder_last_hidden_state=outputs.last_hidden_state,  # index 0 of encoder outputs
+                encoder_hidden_states=outputs.encoder_hidden_states,  # 1 of e out
+                encoder_attentions=outputs.encoder_attentions,  # 2 of e out
+            )
+        else:
+            if past is not None:
+                decoder_outputs = (past,)
+            else:
+                decoder_outputs = tuple(
+                    [x for x in (outputs.decoder_hidden_states, outputs.decoder_attentions) if x is not None]
+                )
+            enc_out = (outputs.encoder_last_hidden_state, outputs.encoder_hidden_states, outputs.encoder_attentions)
+            encoder_outputs = tuple(x for x in enc_out if x is not None)
+            output: Tuple = (logits,) + decoder_outputs + encoder_outputs
+            return ((loss,) + output) if loss is not None else output

    def prepare_inputs_for_generation(self, decoder_input_ids, past, attention_mask, use_cache=True, **kwargs) -> Dict:
        assert past is not None and len(past) in {1, 2}, f"past has to be an iterable of length 1,2 got {past}"
@@ -1163,7 +1175,7 @@ class TFBartForConditionalGeneration(TFPretrainedBartModel):
            encoder_outputs, TFBaseModelOutput
        ), f"encoder_outputs should be a TFBaseModelOutput, Instead got {type(encoder_outputs)}."
        return {
-            "input_ids": None,  # encoder_outputs is defined. input_ids not needed
+            "inputs": None,  # encoder_outputs is defined. input_ids not needed
            "encoder_outputs": encoder_outputs,
            "past_key_values": decoder_cached_states,
            "decoder_input_ids": decoder_input_ids,
@@ -25,17 +25,19 @@ from ..roberta.tokenization_roberta import RobertaTokenizer
 logger = logging.get_logger(__name__)


-# vocab and merges same as roberta
-vocab_url = "https://huggingface.co/roberta-large/resolve/main/vocab.json"
-merges_url = "https://huggingface.co/roberta-large/resolve/main/merges.txt"
-_all_bart_models = [
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "merges_file": "merges.txt",
+}
+
+BART_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
    "facebook/bart-base",
    "facebook/bart-large",
    "facebook/bart-large-mnli",
    "facebook/bart-large-cnn",
    "facebook/bart-large-xsum",
    "yjernite/bart_eli5",
-    # This is not exhaustive: see https://huggingface.co/models?filter=bart
+    # See all BART models at https://huggingface.co/models?filter=bart
 ]


@@ -49,12 +51,8 @@ class BartTokenizer(RobertaTokenizer):
    Refer to superclass :class:`~transformers.RobertaTokenizer` for usage examples and documentation concerning the
    initialization parameters and other methods.
    """
-    # merges and vocab same as Roberta
-    max_model_input_sizes = {m: 1024 for m in _all_bart_models}
-    pretrained_vocab_files_map = {
-        "vocab_file": {m: vocab_url for m in _all_bart_models},
-        "merges_file": {m: merges_url for m in _all_bart_models},
-    }
+    vocab_files_names = VOCAB_FILES_NAMES
+    max_model_input_sizes = 1024

    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
    def prepare_seq2seq_batch(
@@ -26,29 +26,26 @@ from .tokenization_bart import BartTokenizer
 logger = logging.get_logger(__name__)


-# vocab and merges same as roberta
-vocab_url = "https://huggingface.co/roberta-large/resolve/main/vocab.json"
-merges_url = "https://huggingface.co/roberta-large/resolve/main/merges.txt"
-tokenizer_url = "https://huggingface.co/roberta-large/resolve/main/tokenizer.json"
-_all_bart_models = [
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "merges_file": "merges.txt",
+    "tokenizer_file": "tokenizer.json"
+}
+
+BART_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
    "facebook/bart-base",
    "facebook/bart-large",
    "facebook/bart-large-mnli",
    "facebook/bart-large-cnn",
    "facebook/bart-large-xsum",
    "yjernite/bart_eli5",
-    # This is not exhaustive: see https://huggingface.co/models?filter=bart
+    # See all BART models at https://huggingface.co/models?filter=bart
 ]


 class BartTokenizerFast(RobertaTokenizerFast):
-    # merges and vocab same as Roberta
-    max_model_input_sizes = {m: 1024 for m in _all_bart_models}
-    pretrained_vocab_files_map = {
-        "vocab_file": {m: vocab_url for m in _all_bart_models},
-        "merges_file": {m: merges_url for m in _all_bart_models},
-        "tokenizer_file": {m: tokenizer_url for m in _all_bart_models},
-    }
+    vocab_files_names = VOCAB_FILES_NAMES
+    max_model_input_sizes = 1024
    slow_tokenizer_class = BartTokenizer

    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
@@ -1,12 +0,0 @@
-# flake8: noqa
-# There's no way to ignore "F401 '...' imported but unused" warnings in this
-# module, but to preserve other warnings. So, don't check this module at all.
-
-from ...file_utils import is_sentencepiece_available, is_tokenizers_available
-
-
-if is_sentencepiece_available():
-    from .tokenization_barthez import BarthezTokenizer
-
-if is_tokenizers_available():
-    from .tokenization_barthez_fast import BarthezTokenizerFast
@@ -1,308 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Ecole Polytechnique and the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on 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
-""" Tokenization classes for the BARThez model."""
-
-
-import os
-from shutil import copyfile
-from typing import List, Optional, Tuple
-
-import sentencepiece as spm
-
-from ...file_utils import add_start_docstrings
-from ...tokenization_utils import PreTrainedTokenizer
-from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
-from ...utils import logging
-
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
-        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
-        "moussaKam/barthez-orangesum-title": "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "moussaKam/mbarthez": 1024,
-    "moussaKam/barthez": 1024,
-    "moussaKam/barthez-orangesum-title": 1024,
-}
-
-SPIECE_UNDERLINE = "▁"
-
-
-class BarthezTokenizer(PreTrainedTokenizer):
-    """
-    Adapted from :class:`~transformers.CamembertTokenizer` and :class:`~transformers.BartTokenizer`. Construct a
-    BARThez tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.
-
-    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
-    Users should refer to this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (:obj:`str`):
-            `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
-            contains the vocabulary necessary to instantiate a tokenizer.
-        bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            .. note::
-
-                When building a sequence using special tokens, this is not the token that is used for the beginning of
-                sequence. The token used is the :obj:`cls_token`.
-        eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
-            The end of sequence token.
-
-            .. note::
-
-                When building a sequence using special tokens, this is not the token that is used for the end of
-                sequence. The token used is the :obj:`sep_token`.
-        sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        cls_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        additional_special_tokens (:obj:`List[str]`, `optional`, defaults to :obj:`["<s>NOTUSED", "</s>NOTUSED"]`):
-            Additional special tokens used by the tokenizer.
-
-    Attributes: sp_model (:obj:`SentencePieceProcessor`): The `SentencePiece` processor that is used for every
-    conversion (string, tokens and IDs).
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        **kwargs
-    ):
-        super().__init__(
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            **kwargs,
-        )
-
-        self.vocab_file = vocab_file
-        self.sp_model = spm.SentencePieceProcessor()
-        self.sp_model.Load(str(vocab_file))
-
-        self.fairseq_tokens_to_ids = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
-
-        self.fairseq_tokens_to_ids["<mask>"] = len(self.sp_model) - 1
-        self.fairseq_ids_to_tokens = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BARThez sequence has the following format:
-
-        - single sequence: ``<s> X </s>``
-        - pair of sequences: ``<s> A </s></s> B </s>``
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
-        """
-
-        if token_ids_1 is None:
-            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer ``prepare_for_model`` method.
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formated with special tokens for the model."
-                )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task.
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            :obj:`List[int]`: List of zeros.
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
-
-    @property
-    def vocab_size(self):
-        return len(self.sp_model)
-
-    def get_vocab(self):
-        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
-        vocab.update(self.added_tokens_encoder)
-        return vocab
-
-    def _tokenize(self, text):
-        return self.sp_model.EncodeAsPieces(text)
-
-    def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
-        if token in self.fairseq_tokens_to_ids:
-            return self.fairseq_tokens_to_ids[token]
-        spm_id = self.sp_model.PieceToId(token)
-
-        return spm_id if spm_id else self.unk_token_id
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        if index in self.fairseq_ids_to_tokens:
-            return self.fairseq_ids_to_tokens[index]
-        return self.sp_model.IdToPiece(index)
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state["sp_model"] = None
-        return state
-
-    def __setstate__(self, d):
-        self.__dict__ = d
-        self.sp_model = spm.SentencePieceProcessor()
-        self.sp_model.Load(self.vocab_file)
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (strings for sub-words) in a single string."""
-        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
-        return out_string
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)
-
-    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
-    def prepare_seq2seq_batch(
-        self,
-        src_texts: List[str],
-        tgt_texts: Optional[List[str]] = None,
-        max_length: Optional[int] = None,
-        max_target_length: Optional[int] = None,
-        padding: str = "longest",
-        return_tensors: str = "None",
-        truncation=True,
-        **kwargs,
-    ) -> BatchEncoding:
-        kwargs.pop("src_lang", None)
-        kwargs.pop("tgt_lang", None)
-        if max_length is None:
-            max_length = self.model_max_length
-        model_inputs: BatchEncoding = self(
-            src_texts,
-            add_special_tokens=True,
-            return_tensors=return_tensors,
-            max_length=max_length,
-            padding=padding,
-            truncation=truncation,
-            **kwargs,
-        )
-        if tgt_texts is None:
-            return model_inputs
-        # Process tgt_texts
-        if max_target_length is None:
-            max_target_length = max_length
-        labels = self(
-            tgt_texts,
-            add_special_tokens=True,
-            return_tensors=return_tensors,
-            padding=padding,
-            max_length=max_target_length,
-            truncation=truncation,
-            **kwargs,
-        )["input_ids"]
-        model_inputs["labels"] = labels
-        return model_inputs
@@ -1,272 +0,0 @@
-# coding=utf-8
-# Copyright 2020 Ecole Polytechnique and the HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on 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
-""" Tokenization classes for the BARThez model."""
-
-
-import os
-from shutil import copyfile
-from typing import List, Optional, Tuple
-
-from ...file_utils import add_start_docstrings, is_sentencepiece_available
-from ...tokenization_utils_base import PREPARE_SEQ2SEQ_BATCH_DOCSTRING, BatchEncoding
-from ...tokenization_utils_fast import PreTrainedTokenizerFast
-from ...utils import logging
-
-
-if is_sentencepiece_available():
-    from .tokenization_barthez import BarthezTokenizer
-else:
-    BarthezTokenizer = None
-
-logger = logging.get_logger(__name__)
-
-VOCAB_FILES_NAMES = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
-
-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
-        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
-        "moussaKam/barthez-orangesum-title": "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model",
-    },
-    "tokenizer_file": {
-        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
-        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
-        "moussaKam/barthez-orangesum-title": "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "moussaKam/mbarthez": 1024,
-    "moussaKam/barthez": 1024,
-    "moussaKam/barthez-orangesum-title": 1024,
-}
-
-SPIECE_UNDERLINE = "▁"
-
-
-class BarthezTokenizerFast(PreTrainedTokenizerFast):
-    """
-    Adapted from :class:`~transformers.CamembertTokenizer` and :class:`~transformers.BartTokenizer`. Construct a "fast"
-    BARThez tokenizer. Based on `SentencePiece <https://github.com/google/sentencepiece>`__.
-
-    This tokenizer inherits from :class:`~transformers.PreTrainedTokenizerFast` which contains most of the main
-    methods. Users should refer to this superclass for more information regarding those methods.
-
-    Args:
-        vocab_file (:obj:`str`):
-            `SentencePiece <https://github.com/google/sentencepiece>`__ file (generally has a `.spm` extension) that
-            contains the vocabulary necessary to instantiate a tokenizer.
-        bos_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
-            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
-
-            .. note::
-
-                When building a sequence using special tokens, this is not the token that is used for the beginning of
-                sequence. The token used is the :obj:`cls_token`.
-        eos_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
-            The end of sequence token.
-
-            .. note::
-
-                When building a sequence using special tokens, this is not the token that is used for the end of
-                sequence. The token used is the :obj:`sep_token`.
-        sep_token (:obj:`str`, `optional`, defaults to :obj:`"</s>"`):
-            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
-            sequence classification or for a text and a question for question answering. It is also used as the last
-            token of a sequence built with special tokens.
-        cls_token (:obj:`str`, `optional`, defaults to :obj:`"<s>"`):
-            The classifier token which is used when doing sequence classification (classification of the whole sequence
-            instead of per-token classification). It is the first token of the sequence when built with special tokens.
-        unk_token (:obj:`str`, `optional`, defaults to :obj:`"<unk>"`):
-            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
-            token instead.
-        pad_token (:obj:`str`, `optional`, defaults to :obj:`"<pad>"`):
-            The token used for padding, for example when batching sequences of different lengths.
-        mask_token (:obj:`str`, `optional`, defaults to :obj:`"<mask>"`):
-            The token used for masking values. This is the token used when training this model with masked language
-            modeling. This is the token which the model will try to predict.
-        additional_special_tokens (:obj:`List[str]`, `optional`, defaults to :obj:`["<s>NOTUSED", "</s>NOTUSED"]`):
-            Additional special tokens used by the tokenizer.
-
-    Attributes: sp_model (:obj:`SentencePieceProcessor`): The `SentencePiece` processor that is used for every
-    conversion (string, tokens and IDs).
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
-    model_input_names = ["attention_mask"]
-    slow_tokenizer_class = BarthezTokenizer
-
-    def __init__(
-        self,
-        vocab_file,
-        tokenizer_file=None,
-        bos_token="<s>",
-        eos_token="</s>",
-        sep_token="</s>",
-        cls_token="<s>",
-        unk_token="<unk>",
-        pad_token="<pad>",
-        mask_token="<mask>",
-        **kwargs
-    ):
-        super().__init__(
-            vocab_file,
-            tokenizer_file=tokenizer_file,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            unk_token=unk_token,
-            sep_token=sep_token,
-            cls_token=cls_token,
-            pad_token=pad_token,
-            mask_token=mask_token,
-            **kwargs,
-        )
-
-        self.vocab_file = vocab_file
-
-    def build_inputs_with_special_tokens(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
-        adding special tokens. A BARThez sequence has the following format:
-
-        - single sequence: ``<s> X </s>``
-        - pair of sequences: ``<s> A </s></s> B </s>``
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs to which the special tokens will be added.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            :obj:`List[int]`: List of `input IDs <../glossary.html#input-ids>`__ with the appropriate special tokens.
-        """
-
-        if token_ids_1 is None:
-            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
-        cls = [self.cls_token_id]
-        sep = [self.sep_token_id]
-        return cls + token_ids_0 + sep + sep + token_ids_1 + sep
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer ``prepare_for_model`` method.
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            :obj:`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            if token_ids_1 is not None:
-                raise ValueError(
-                    "You should not supply a second sequence if the provided sequence of "
-                    "ids is already formated with special tokens for the model."
-                )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0))
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0)) + [1]
-        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
-
-    def create_token_type_ids_from_sequences(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
-    ) -> List[int]:
-        """
-        Create a mask from the two sequences passed to be used in a sequence-pair classification task.
-
-        Args:
-            token_ids_0 (:obj:`List[int]`):
-                List of IDs.
-            token_ids_1 (:obj:`List[int]`, `optional`):
-                Optional second list of IDs for sequence pairs.
-
-        Returns:
-            :obj:`List[int]`: List of zeros.
-        """
-        sep = [self.sep_token_id]
-        cls = [self.cls_token_id]
-
-        if token_ids_1 is None:
-            return len(cls + token_ids_0 + sep) * [0]
-        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error("Vocabulary path ({}) should be a directory".format(save_directory))
-            return
-        out_vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-
-        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
-            copyfile(self.vocab_file, out_vocab_file)
-
-        return (out_vocab_file,)
-
-    @add_start_docstrings(PREPARE_SEQ2SEQ_BATCH_DOCSTRING)
-    def prepare_seq2seq_batch(
-        self,
-        src_texts: List[str],
-        tgt_texts: Optional[List[str]] = None,
-        max_length: Optional[int] = None,
-        max_target_length: Optional[int] = None,
-        padding: str = "longest",
-        return_tensors: str = "None",
-        truncation=True,
-        **kwargs,
-    ) -> BatchEncoding:
-        kwargs.pop("src_lang", None)
-        kwargs.pop("tgt_lang", None)
-        if max_length is None:
-            max_length = self.model_max_length
-        model_inputs: BatchEncoding = self(
-            src_texts,
-            add_special_tokens=True,
-            return_tensors=return_tensors,
-            max_length=max_length,
-            padding=padding,
-            truncation=truncation,
-            **kwargs,
-        )
-        if tgt_texts is None:
-            return model_inputs
-        # Process tgt_texts
-        if max_target_length is None:
-            max_target_length = max_length
-        labels = self(
-            tgt_texts,
-            add_special_tokens=True,
-            return_tensors=return_tensors,
-            padding=padding,
-            max_length=max_target_length,
-            truncation=truncation,
-            **kwargs,
-        )["input_ids"]
-        model_inputs["labels"] = labels
-        return model_inputs
@@ -4,11 +4,11 @@

 from ...file_utils import is_flax_available, is_tf_available, is_tokenizers_available, is_torch_available
 from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
-from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer
+from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer, BERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST


 if is_tokenizers_available():
-    from .tokenization_bert_fast import BertTokenizerFast
+    from .tokenization_bert_fast import BertTokenizerFast, BERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST

 if is_torch_available():
    from .modeling_bert import (
@@ -91,13 +91,6 @@ class BertConfig(PretrainedConfig):
            The epsilon used by the layer normalization layers.
        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If True, use gradient checkpointing to save memory at the expense of slower backward pass.
-        position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
-            Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
-            :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
-            :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
-            <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
-            `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
-            <https://arxiv.org/abs/2009.13658>`__.

    Examples::

@@ -130,7 +123,6 @@ class BertConfig(PretrainedConfig):
        layer_norm_eps=1e-12,
        pad_token_id=0,
        gradient_checkpointing=False,
-        position_embedding_type="absolute",
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, **kwargs)
@@ -148,4 +140,3 @@ class BertConfig(PretrainedConfig):
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.gradient_checkpointing = gradient_checkpointing
-        self.position_embedding_type = position_embedding_type
@@ -178,7 +178,6 @@ class BertEmbeddings(nn.Module):

        # position_ids (1, len position emb) is contiguous in memory and exported when serialized
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")

    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if input_ids is not None:
@@ -196,12 +195,10 @@ class BertEmbeddings(nn.Module):

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)

-        embeddings = inputs_embeds + token_type_embeddings
-        if self.position_embedding_type == "absolute":
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings += position_embeddings
+        embeddings = inputs_embeds + position_embeddings + token_type_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
@@ -225,10 +222,6 @@ class BertSelfAttention(nn.Module):
        self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
-        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            self.max_position_embeddings = config.max_position_embeddings
-            self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size)

    def transpose_for_scores(self, x):
        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
@@ -263,23 +256,6 @@ class BertSelfAttention(nn.Module):

        # Take the dot product between "query" and "key" to get the raw attention scores.
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
-
-        if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
-            seq_length = hidden_states.size()[1]
-            position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1)
-            position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1)
-            distance = position_ids_l - position_ids_r
-            positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1)
-            positional_embedding = positional_embedding.to(dtype=query_layer.dtype)  # fp16 compatibility
-
-            if self.position_embedding_type == "relative_key":
-                relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores
-            elif self.position_embedding_type == "relative_key_query":
-                relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
-                relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding)
-                attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key
-
        attention_scores = attention_scores / math.sqrt(self.attention_head_size)
        if attention_mask is not None:
            # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
@@ -622,7 +598,7 @@ class BertPreTrainedModel(PreTrainedModel):
    config_class = BertConfig
    load_tf_weights = load_tf_weights_in_bert
    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
+    authorized_missing_keys = [r"position_ids"]

    def _init_weights(self, module):
        """ Initialize the weights """
@@ -888,7 +864,7 @@ class BertModel(BertPreTrainedModel):

@add_start_docstrings(
    """
-    Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
+    Bert Model with two heads on top as done during the pre-training: a `masked language modeling` head and a `next
    sentence prediction (classification)` head.
    """,
    BERT_START_DOCSTRING,
@@ -993,8 +969,8 @@ class BertForPreTraining(BertPreTrainedModel):
 )
 class BertLMHeadModel(BertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    authorized_unexpected_keys = [r"pooler"]
+    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)
@@ -1111,8 +1087,8 @@ class BertLMHeadModel(BertPreTrainedModel):
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
 class BertForMaskedLM(BertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+    authorized_unexpected_keys = [r"pooler"]
+    authorized_missing_keys = [r"position_ids", r"predictions.decoder.bias"]

    def __init__(self, config):
        super().__init__(config)
@@ -1493,7 +1469,7 @@ class BertForMultipleChoice(BertPreTrainedModel):
 )
 class BertForTokenClassification(BertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
@@ -1584,7 +1560,7 @@ class BertForTokenClassification(BertPreTrainedModel):
 )
 class BertForQuestionAnswering(BertPreTrainedModel):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]

    def __init__(self, config):
        super().__init__(config)
@@ -183,10 +183,6 @@ class FlaxBertAttention(nn.Module):

    @nn.compact
    def __call__(self, hidden_state, attention_mask):
-        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
-        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
-        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
-        attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
        self_att = nn.attention.SelfAttention(num_heads=self.num_heads, qkv_features=self.head_size, name="self")(
            hidden_state, attention_mask
        )
@@ -15,6 +15,7 @@
 # limitations under the License.
 """ TF 2.0 BERT model. """

+
 from dataclasses import dataclass
 from typing import Optional, Tuple

@@ -50,10 +51,10 @@ from ...modeling_tf_utils import (
    TFSequenceClassificationLoss,
    TFTokenClassificationLoss,
    get_initializer,
-    input_processing,
    keras_serializable,
    shape_list,
 )
+from ...tokenization_utils import BatchEncoding
 from ...utils import logging
 from .configuration_bert import BertConfig

@@ -90,7 +91,7 @@ TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST = [

 class TFBertPreTrainingLoss:
    """
-    Loss function suitable for BERT-like pretraining, that is, the task of pretraining a language model by combining
+    Loss function suitable for BERT-like pre-training, that is, the task of pretraining a language model by combining
    NSP + MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss
    computation.
    """
@@ -575,7 +576,7 @@ class TFBertMainLayer(tf.keras.layers.Layer):

    def call(
        self,
-        input_ids=None,
+        inputs,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -585,59 +586,59 @@ class TFBertMainLayer(tf.keras.layers.Layer):
        output_hidden_states=None,
        return_dict=None,
        training=False,
-        **kwargs,
    ):
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-            kwargs_call=kwargs,
-        )
-        output_attentions = (
-            inputs["output_attentions"] if inputs["output_attentions"] is not None else self.output_attentions
-        )
-        output_hidden_states = (
-            inputs["output_hidden_states"] if inputs["output_hidden_states"] is not None else self.output_hidden_states
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.return_dict
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
+            position_ids = inputs[3] if len(inputs) > 3 else position_ids
+            head_mask = inputs[4] if len(inputs) > 4 else head_mask
+            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            return_dict = inputs[8] if len(inputs) > 8 else return_dict
+            assert len(inputs) <= 9, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            token_type_ids = inputs.get("token_type_ids", token_type_ids)
+            position_ids = inputs.get("position_ids", position_ids)
+            head_mask = inputs.get("head_mask", head_mask)
+            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            return_dict = inputs.get("return_dict", return_dict)
+            assert len(inputs) <= 9, "Too many inputs."
+        else:
+            input_ids = inputs

-        if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
+        output_attentions = output_attentions if output_attentions is not None else self.output_attentions
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        elif inputs["input_ids"] is not None:
-            input_shape = shape_list(inputs["input_ids"])
-        elif inputs["inputs_embeds"] is not None:
-            input_shape = shape_list(inputs["inputs_embeds"])[:-1]
+        elif input_ids is not None:
+            input_shape = shape_list(input_ids)
+        elif inputs_embeds is not None:
+            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

-        if inputs["attention_mask"] is None:
-            inputs["attention_mask"] = tf.fill(input_shape, 1)
+        if attention_mask is None:
+            attention_mask = tf.fill(input_shape, 1)

-        if inputs["token_type_ids"] is None:
-            inputs["token_type_ids"] = tf.fill(input_shape, 0)
+        if token_type_ids is None:
+            token_type_ids = tf.fill(input_shape, 0)

-        embedding_output = self.embeddings(
-            inputs["input_ids"],
-            inputs["position_ids"],
-            inputs["token_type_ids"],
-            inputs["inputs_embeds"],
-            training=inputs["training"],
-        )
+        embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)

        # We create a 3D attention mask from a 2D tensor mask.
        # Sizes are [batch_size, 1, 1, to_seq_length]
        # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
        # this attention mask is more simple than the triangular masking of causal attention
        # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
-        extended_attention_mask = inputs["attention_mask"][:, tf.newaxis, tf.newaxis, :]
+        extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]

        # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
        # masked positions, this operation will create a tensor which is 0.0 for
@@ -652,19 +653,20 @@ class TFBertMainLayer(tf.keras.layers.Layer):
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
-        if inputs["head_mask"] is not None:
+        if head_mask is not None:
            raise NotImplementedError
        else:
-            inputs["head_mask"] = [None] * self.num_hidden_layers
+            head_mask = [None] * self.num_hidden_layers
+            # head_mask = tf.constant([0] * self.num_hidden_layers)

        encoder_outputs = self.encoder(
            embedding_output,
            extended_attention_mask,
-            inputs["head_mask"],
+            head_mask,
            output_attentions,
            output_hidden_states,
            return_dict,
-            training=inputs["training"],
+            training=training,
        )

        sequence_output = encoder_outputs[0]
@@ -832,53 +834,15 @@ class TFBertModel(TFBertPreTrainedModel):
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
    )
-    def call(
-        self,
-        input_ids=None,
-        attention_mask=None,
-        token_type_ids=None,
-        position_ids=None,
-        head_mask=None,
-        inputs_embeds=None,
-        output_attentions=None,
-        output_hidden_states=None,
-        return_dict=None,
-        training=False,
-        **kwargs,
-    ):
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            training=training,
-            kwargs_call=kwargs,
-        )
-        outputs = self.bert(
-            input_ids=inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=inputs["return_dict"],
-            training=inputs["training"],
-        )
+    def call(self, inputs, **kwargs):
+        outputs = self.bert(inputs, **kwargs)

        return outputs


@add_start_docstrings(
    """
-Bert Model with two heads on top as done during the pretraining:
+Bert Model with two heads on top as done during the pre-training:
    a `masked language modeling` head and a `next sentence prediction (classification)` head.
    """,
    BERT_START_DOCSTRING,
@@ -898,7 +862,7 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
    @replace_return_docstrings(output_type=TFBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -910,7 +874,6 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
        labels=None,
        next_sentence_label=None,
        training=False,
-        **kwargs,
    ):
        r"""
        Return:
@@ -927,9 +890,19 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
            >>> prediction_scores, seq_relationship_scores = outputs[:2]

        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            next_sentence_label = inputs[10] if len(inputs) > 10 else next_sentence_label
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+            next_sentence_label = inputs.pop("next_sentence_label", next_sentence_label)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -938,32 +911,16 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
-            next_sentence_label=next_sentence_label,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
        sequence_output, pooled_output = outputs[:2]
-        prediction_scores = self.mlm(sequence_output, training=inputs["training"])
+        prediction_scores = self.mlm(sequence_output, training=training)
        seq_relationship_score = self.nsp(pooled_output)
        total_loss = None

-        if inputs["labels"] is not None and inputs["next_sentence_label"] is not None:
-            d_labels = {"labels": inputs["labels"]}
-            d_labels["next_sentence_label"] = inputs["next_sentence_label"]
+        if labels is not None and next_sentence_label is not None:
+            d_labels = {"labels": labels}
+            d_labels["next_sentence_label"] = next_sentence_label
            total_loss = self.compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))

        if not return_dict:
@@ -981,8 +938,8 @@ class TFBertForPreTraining(TFBertPreTrainedModel, TFBertPreTrainingLoss):
@add_start_docstrings("""Bert Model with a `language modeling` head on top. """, BERT_START_DOCSTRING)
 class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -1008,7 +965,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1019,7 +976,6 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
@@ -1027,9 +983,17 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
            config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
            (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1038,26 +1002,12 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
+
        sequence_output = outputs[0]
-        prediction_scores = self.mlm(sequence_output, training=inputs["training"])
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], prediction_scores)
+        prediction_scores = self.mlm(sequence_output, training=training)
+        loss = None if labels is None else self.compute_loss(labels, prediction_scores)

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
@@ -1073,8 +1023,8 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):

 class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -1096,7 +1046,7 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1107,16 +1057,23 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the cross entropy classification loss. Indices should be in ``[0, ...,
            config.vocab_size - 1]``.
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1125,31 +1082,17 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
+
        sequence_output = outputs[0]
-        logits = self.mlm(sequence_output, training=inputs["training"])
+        logits = self.mlm(sequence_output, training=training)
        loss = None

-        if inputs["labels"] is not None:
+        if labels is not None:
            # shift labels to the left and cut last logit token
            logits = logits[:, :-1]
-            labels = inputs["labels"][:, 1:]
+            labels = labels[:, 1:]
            loss = self.compute_loss(labels, logits)

        if not return_dict:
@@ -1179,7 +1122,7 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredi
    @replace_return_docstrings(output_type=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1190,7 +1133,6 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredi
        return_dict=None,
        next_sentence_label=None,
        training=False,
-        **kwargs,
    ):
        r"""
        Return:
@@ -1210,9 +1152,17 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredi
            >>> logits = model(encoding['input_ids'], token_type_ids=encoding['token_type_ids'])[0]
            >>> assert logits[0][0] < logits[0][1] # the next sentence was random
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            next_sentence_label = inputs[9] if len(inputs) > 9 else next_sentence_label
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            next_sentence_label = inputs.pop("next_sentence_label", next_sentence_label)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1221,29 +1171,15 @@ class TFBertForNextSentencePrediction(TFBertPreTrainedModel, TFNextSentencePredi
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            next_sentence_label=next_sentence_label,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
        pooled_output = outputs[1]
        seq_relationship_scores = self.nsp(pooled_output)
+
        next_sentence_loss = (
            None
-            if inputs["next_sentence_label"] is None
-            else self.compute_loss(labels=inputs["next_sentence_label"], logits=seq_relationship_scores)
+            if next_sentence_label is None
+            else self.compute_loss(labels=next_sentence_label, logits=seq_relationship_scores)
        )

        if not return_dict:
@@ -1285,7 +1221,7 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1296,7 +1232,6 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1304,9 +1239,17 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
            config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1315,27 +1258,13 @@ class TFBertForSequenceClassification(TFBertPreTrainedModel, TFSequenceClassific
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
+
        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output, training=inputs["training"])
+        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
+        loss = None if labels is None else self.compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[2:]
@@ -1385,7 +1314,7 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
    )
    def call(
        self,
-        input_ids=None,
+        inputs,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1396,7 +1325,6 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1404,43 +1332,49 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
            num_choices]`` where :obj:`num_choices` is the size of the second dimension of the input tensors. (See
            :obj:`input_ids` above)
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-            labels=labels,
-            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-
-        if inputs["input_ids"] is not None:
-            num_choices = shape_list(inputs["input_ids"])[1]
-            seq_length = shape_list(inputs["input_ids"])[2]
+        if isinstance(inputs, (tuple, list)):
+            input_ids = inputs[0]
+            attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
+            token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
+            position_ids = inputs[3] if len(inputs) > 3 else position_ids
+            head_mask = inputs[4] if len(inputs) > 4 else head_mask
+            inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
+            output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
+            output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
+            return_dict = inputs[8] if len(inputs) > 8 else return_dict
+            labels = inputs[9] if len(inputs) > 9 else labels
+            assert len(inputs) <= 10, "Too many inputs."
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            input_ids = inputs.get("input_ids")
+            attention_mask = inputs.get("attention_mask", attention_mask)
+            token_type_ids = inputs.get("token_type_ids", token_type_ids)
+            position_ids = inputs.get("position_ids", position_ids)
+            head_mask = inputs.get("head_mask", head_mask)
+            inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
+            output_attentions = inputs.get("output_attentions", output_attentions)
+            output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
+            return_dict = inputs.get("return_dict", return_dict)
+            labels = inputs.get("labels", labels)
+            assert len(inputs) <= 10, "Too many inputs."
        else:
-            num_choices = shape_list(inputs["inputs_embeds"])[1]
-            seq_length = shape_list(inputs["inputs_embeds"])[2]
+            input_ids = inputs

-        flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
-        flat_attention_mask = (
-            tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
-        )
-        flat_token_type_ids = (
-            tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
-        )
-        flat_position_ids = (
-            tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None
-        )
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if input_ids is not None:
+            num_choices = shape_list(input_ids)[1]
+            seq_length = shape_list(input_ids)[2]
+        else:
+            num_choices = shape_list(inputs_embeds)[1]
+            seq_length = shape_list(inputs_embeds)[2]
+
+        flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
+        flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
+        flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
+        flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
        flat_inputs_embeds = (
-            tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
-            if inputs["inputs_embeds"] is not None
+            tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
+            if inputs_embeds is not None
            else None
        )
        outputs = self.bert(
@@ -1448,18 +1382,18 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
            flat_attention_mask,
            flat_token_type_ids,
            flat_position_ids,
-            inputs["head_mask"],
+            head_mask,
            flat_inputs_embeds,
-            inputs["output_attentions"],
-            inputs["output_hidden_states"],
+            output_attentions,
+            output_hidden_states,
            return_dict=return_dict,
-            training=inputs["training"],
+            training=training,
        )
        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output, training=inputs["training"])
+        pooled_output = self.dropout(pooled_output, training=training)
        logits = self.classifier(pooled_output)
        reshaped_logits = tf.reshape(logits, (-1, num_choices))
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], reshaped_logits)
+        loss = None if labels is None else self.compute_loss(labels, reshaped_logits)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
@@ -1482,8 +1416,8 @@ class TFBertForMultipleChoice(TFBertPreTrainedModel, TFMultipleChoiceLoss):
 )
 class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationLoss):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -1504,7 +1438,7 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1515,16 +1449,23 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
        return_dict=None,
        labels=None,
        training=False,
-        **kwargs,
    ):
        r"""
        labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
            Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -
            1]``.
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            labels = inputs[9] if len(inputs) > 9 else labels
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            labels = inputs.pop("labels", labels)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1533,27 +1474,12 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            labels=labels,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
        sequence_output = outputs[0]
-        sequence_output = self.dropout(sequence_output, training=inputs["training"])
+        sequence_output = self.dropout(sequence_output, training=training)
        logits = self.classifier(sequence_output)
-        loss = None if inputs["labels"] is None else self.compute_loss(inputs["labels"], logits)
+        loss = None if labels is None else self.compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[2:]
@@ -1576,8 +1502,8 @@ class TFBertForTokenClassification(TFBertPreTrainedModel, TFTokenClassificationL
 )
 class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss):

-    _keys_to_ignore_on_load_unexpected = [r"pooler"]
-    _keys_to_ignore_on_load_missing = [r"pooler"]
+    authorized_unexpected_keys = [r"pooler"]
+    authorized_missing_keys = [r"pooler"]

    def __init__(self, config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)
@@ -1597,7 +1523,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
    )
    def call(
        self,
-        input_ids=None,
+        inputs=None,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
@@ -1609,7 +1535,6 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
        start_positions=None,
        end_positions=None,
        training=False,
-        **kwargs,
    ):
        r"""
        start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
@@ -1621,9 +1546,19 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
            Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the
            sequence are not taken into account for computing the loss.
        """
-        inputs = input_processing(
-            func=self.call,
-            input_ids=input_ids,
+        return_dict = return_dict if return_dict is not None else self.bert.return_dict
+
+        if isinstance(inputs, (tuple, list)):
+            start_positions = inputs[9] if len(inputs) > 9 else start_positions
+            end_positions = inputs[10] if len(inputs) > 10 else end_positions
+            if len(inputs) > 9:
+                inputs = inputs[:9]
+        elif isinstance(inputs, (dict, BatchEncoding)):
+            start_positions = inputs.pop("start_positions", start_positions)
+            end_positions = inputs.pop("end_positions", start_positions)
+
+        outputs = self.bert(
+            inputs,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
@@ -1632,23 +1567,7 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
-            start_positions=start_positions,
-            end_positions=end_positions,
            training=training,
-            kwargs_call=kwargs,
-        )
-        return_dict = inputs["return_dict"] if inputs["return_dict"] is not None else self.bert.return_dict
-        outputs = self.bert(
-            inputs["input_ids"],
-            attention_mask=inputs["attention_mask"],
-            token_type_ids=inputs["token_type_ids"],
-            position_ids=inputs["position_ids"],
-            head_mask=inputs["head_mask"],
-            inputs_embeds=inputs["inputs_embeds"],
-            output_attentions=inputs["output_attentions"],
-            output_hidden_states=inputs["output_hidden_states"],
-            return_dict=return_dict,
-            training=inputs["training"],
        )
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
@@ -1657,9 +1576,9 @@ class TFBertForQuestionAnswering(TFBertPreTrainedModel, TFQuestionAnsweringLoss)
        end_logits = tf.squeeze(end_logits, axis=-1)
        loss = None

-        if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
-            labels = {"start_position": inputs["start_positions"]}
-            labels["end_position"] = inputs["end_positions"]
+        if start_positions is not None and end_positions is not None:
+            labels = {"start_position": start_positions}
+            labels["end_position"] = end_positions
            loss = self.compute_loss(labels, (start_logits, end_logits))

        if not return_dict:
@@ -28,70 +28,27 @@ logger = logging.get_logger(__name__)

 VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
-        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
-        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
-        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
-        "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt",
-        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
-        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
-        "bert-base-german-cased": "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
-        "bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt",
-        "bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt",
-        "bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt",
-        "bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt",
-        "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt",
-        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
-        "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt",
-        "TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt",
-        "TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt",
-        "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt",
-    }
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "bert-base-uncased": 512,
-    "bert-large-uncased": 512,
-    "bert-base-cased": 512,
-    "bert-large-cased": 512,
-    "bert-base-multilingual-uncased": 512,
-    "bert-base-multilingual-cased": 512,
-    "bert-base-chinese": 512,
-    "bert-base-german-cased": 512,
-    "bert-large-uncased-whole-word-masking": 512,
-    "bert-large-cased-whole-word-masking": 512,
-    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
-    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
-    "bert-base-cased-finetuned-mrpc": 512,
-    "bert-base-german-dbmdz-cased": 512,
-    "bert-base-german-dbmdz-uncased": 512,
-    "TurkuNLP/bert-base-finnish-cased-v1": 512,
-    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
-    "wietsedv/bert-base-dutch-cased": 512,
-}
-
-PRETRAINED_INIT_CONFIGURATION = {
-    "bert-base-uncased": {"do_lower_case": True},
-    "bert-large-uncased": {"do_lower_case": True},
-    "bert-base-cased": {"do_lower_case": False},
-    "bert-large-cased": {"do_lower_case": False},
-    "bert-base-multilingual-uncased": {"do_lower_case": True},
-    "bert-base-multilingual-cased": {"do_lower_case": False},
-    "bert-base-chinese": {"do_lower_case": False},
-    "bert-base-german-cased": {"do_lower_case": False},
-    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
-    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
-    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
-    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
-    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
-    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
-    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
-    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
-    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
-    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
-}
+BERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
+    "bert-base-uncased",
+    "bert-large-uncased",
+    "bert-base-cased",
+    "bert-large-cased",
+    "bert-base-multilingual-uncased",
+    "bert-base-multilingual-cased",
+    "bert-base-chinese",
+    "bert-base-german-cased",
+    "bert-large-uncased-whole-word-masking",
+    "bert-large-cased-whole-word-masking",
+    "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "bert-large-cased-whole-word-masking-finetuned-squad",
+    "bert-base-cased-finetuned-mrpc",
+    "bert-base-german-dbmdz-cased",
+    "bert-base-german-dbmdz-uncased",
+    "TurkuNLP/bert-base-finnish-cased-v1",
+    "TurkuNLP/bert-base-finnish-uncased-v1",
+    "wietsedv/bert-base-dutch-cased",
+    # See all BERT models at https://huggingface.co/models?filter=bert
+]


 def load_vocab(vocab_file):
@@ -157,9 +114,7 @@ class BertTokenizer(PreTrainedTokenizer):
    """

    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    max_model_input_sizes = 512

    def __init__(
        self,
@@ -28,90 +28,27 @@ logger = logging.get_logger(__name__)

 VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
-        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
-        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
-        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
-        "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt",
-        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
-        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
-        "bert-base-german-cased": "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-vocab.txt",
-        "bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt",
-        "bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt",
-        "bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt",
-        "bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt",
-        "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt",
-        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
-        "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt",
-        "TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt",
-        "TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt",
-        "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt",
-    },
-    "tokenizer_file": {
-        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
-        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
-        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
-        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
-        "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json",
-        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json",
-        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
-        "bert-base-german-cased": "https://int-deepset-models-bert.s3.eu-central-1.amazonaws.com/pytorch/bert-base-german-cased-tokenizer.json",
-        "bert-large-uncased-whole-word-masking": "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json",
-        "bert-large-cased-whole-word-masking": "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json",
-        "bert-large-uncased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json",
-        "bert-large-cased-whole-word-masking-finetuned-squad": "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json",
-        "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json",
-        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json",
-        "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json",
-        "TurkuNLP/bert-base-finnish-cased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json",
-        "TurkuNLP/bert-base-finnish-uncased-v1": "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json",
-        "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json",
-    },
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "bert-base-uncased": 512,
-    "bert-large-uncased": 512,
-    "bert-base-cased": 512,
-    "bert-large-cased": 512,
-    "bert-base-multilingual-uncased": 512,
-    "bert-base-multilingual-cased": 512,
-    "bert-base-chinese": 512,
-    "bert-base-german-cased": 512,
-    "bert-large-uncased-whole-word-masking": 512,
-    "bert-large-cased-whole-word-masking": 512,
-    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
-    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
-    "bert-base-cased-finetuned-mrpc": 512,
-    "bert-base-german-dbmdz-cased": 512,
-    "bert-base-german-dbmdz-uncased": 512,
-    "TurkuNLP/bert-base-finnish-cased-v1": 512,
-    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
-    "wietsedv/bert-base-dutch-cased": 512,
-}
-
-PRETRAINED_INIT_CONFIGURATION = {
-    "bert-base-uncased": {"do_lower_case": True},
-    "bert-large-uncased": {"do_lower_case": True},
-    "bert-base-cased": {"do_lower_case": False},
-    "bert-large-cased": {"do_lower_case": False},
-    "bert-base-multilingual-uncased": {"do_lower_case": True},
-    "bert-base-multilingual-cased": {"do_lower_case": False},
-    "bert-base-chinese": {"do_lower_case": False},
-    "bert-base-german-cased": {"do_lower_case": False},
-    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
-    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
-    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
-    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
-    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
-    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
-    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
-    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
-    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
-    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
-}
+BERT_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
+    "bert-base-uncased",
+    "bert-large-uncased",
+    "bert-base-cased",
+    "bert-large-cased",
+    "bert-base-multilingual-uncased",
+    "bert-base-multilingual-cased",
+    "bert-base-chinese",
+    "bert-base-german-cased",
+    "bert-large-uncased-whole-word-masking",
+    "bert-large-cased-whole-word-masking",
+    "bert-large-uncased-whole-word-masking-finetuned-squad",
+    "bert-large-cased-whole-word-masking-finetuned-squad",
+    "bert-base-cased-finetuned-mrpc",
+    "bert-base-german-dbmdz-cased",
+    "bert-base-german-dbmdz-uncased",
+    "TurkuNLP/bert-base-finnish-cased-v1",
+    "TurkuNLP/bert-base-finnish-uncased-v1",
+    "wietsedv/bert-base-dutch-cased",
+    # See all BERT models at https://huggingface.co/models?filter=bert
+]


 class BertTokenizerFast(PreTrainedTokenizerFast):
@@ -155,9 +92,7 @@ class BertTokenizerFast(PreTrainedTokenizerFast):
    """

    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    max_model_input_sizes = 512
    slow_tokenizer_class = BertTokenizer

    def __init__(
@@ -7,7 +7,7 @@ from .configuration_bert_generation import BertGenerationConfig


 if is_sentencepiece_available():
-    from .tokenization_bert_generation import BertGenerationTokenizer
+    from .tokenization_bert_generation import BertGenerationTokenizer, BERT_GENERATION_PRETRAINED_TOKENIZER_ARCHIVE_LIST

 if is_torch_available():
    from .modeling_bert_generation import (
@@ -54,13 +54,6 @@ class BertGenerationConfig(PretrainedConfig):
            The epsilon used by the layer normalization layers.
        gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
            If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
-        position_embedding_type (:obj:`str`, `optional`, defaults to :obj:`"absolute"`):
-            Type of position embedding. Choose one of :obj:`"absolute"`, :obj:`"relative_key"`,
-            :obj:`"relative_key_query"`. For positional embeddings use :obj:`"absolute"`. For more information on
-            :obj:`"relative_key"`, please refer to `Self-Attention with Relative Position Representations (Shaw et al.)
-            <https://arxiv.org/abs/1803.02155>`__. For more information on :obj:`"relative_key_query"`, please refer to
-            `Method 4` in `Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)
-            <https://arxiv.org/abs/2009.13658>`__.

    Examples::

@@ -94,7 +87,6 @@ class BertGenerationConfig(PretrainedConfig):
        bos_token_id=2,
        eos_token_id=1,
        gradient_checkpointing=False,
-        position_embedding_type="absolute",
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@@ -111,4 +103,3 @@ class BertGenerationConfig(PretrainedConfig):
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.gradient_checkpointing = gradient_checkpointing
-        self.position_embedding_type = position_embedding_type
@@ -173,7 +173,7 @@ class BertGenerationPreTrainedModel(PreTrainedModel):

    config_class = BertGenerationConfig
    base_model_prefix = "bert"
-    _keys_to_ignore_on_load_missing = [r"position_ids"]
+    authorized_missing_keys = [r"position_ids"]

    def _init_weights(self, module):
        """ Initialize the weights """
@@ -29,7 +29,10 @@ logger = logging.get_logger(__name__)

 VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}

-tokenizer_url = "https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model"
+
+BERT_GENERATION_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
+    "google/bert_for_seq_generation_L-24_bbc_encoder",
+]


 class BertGenerationTokenizer(PreTrainedTokenizer):
@@ -55,8 +58,7 @@ class BertGenerationTokenizer(PreTrainedTokenizer):
    """

    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = {"vocab_file": {"bert_for_seq_generation": tokenizer_url}}
-    max_model_input_sizes = {"bert_for_seq_generation": 512}
+    max_model_input_sizes = 512
    prefix_tokens: List[int] = []

    def __init__(
@@ -2,4 +2,4 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.

-from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer
+from .tokenization_bert_japanese import BertJapaneseTokenizer, CharacterTokenizer, MecabTokenizer, BERT_JAPANESE_PRETRAINED_TOKENIZER_ARCHIVE_LIST
@@ -29,53 +29,19 @@ logger = logging.get_logger(__name__)

 VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}

-PRETRAINED_VOCAB_FILES_MAP = {
-    "vocab_file": {
-        "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/vocab.txt",
-        "cl-tohoku/bert-base-japanese-whole-word-masking": "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/vocab.txt",
-        "cl-tohoku/bert-base-japanese-char": "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/vocab.txt",
-        "cl-tohoku/bert-base-japanese-char-whole-word-masking": "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/vocab.txt",
-    }
-}
-
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
-    "cl-tohoku/bert-base-japanese": 512,
-    "cl-tohoku/bert-base-japanese-whole-word-masking": 512,
-    "cl-tohoku/bert-base-japanese-char": 512,
-    "cl-tohoku/bert-base-japanese-char-whole-word-masking": 512,
-}
-
-PRETRAINED_INIT_CONFIGURATION = {
-    "cl-tohoku/bert-base-japanese": {
-        "do_lower_case": False,
-        "word_tokenizer_type": "mecab",
-        "subword_tokenizer_type": "wordpiece",
-    },
-    "cl-tohoku/bert-base-japanese-whole-word-masking": {
-        "do_lower_case": False,
-        "word_tokenizer_type": "mecab",
-        "subword_tokenizer_type": "wordpiece",
-    },
-    "cl-tohoku/bert-base-japanese-char": {
-        "do_lower_case": False,
-        "word_tokenizer_type": "mecab",
-        "subword_tokenizer_type": "character",
-    },
-    "cl-tohoku/bert-base-japanese-char-whole-word-masking": {
-        "do_lower_case": False,
-        "word_tokenizer_type": "mecab",
-        "subword_tokenizer_type": "character",
-    },
-}
+BERT_JAPANESE_PRETRAINED_TOKENIZER_ARCHIVE_LIST = [
+        "cl-tohoku/bert-base-japanese",
+        "cl-tohoku/bert-base-japanese-whole-word-masking",
+        "cl-tohoku/bert-base-japanese-char",
+        "cl-tohoku/bert-base-japanese-char-whole-word-masking",
+]


 class BertJapaneseTokenizer(BertTokenizer):
    """BERT tokenizer for Japanese text"""

    vocab_files_names = VOCAB_FILES_NAMES
-    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
-    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
-    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    max_model_input_sizes = 512

    def __init__(
        self,
@@ -2,4 +2,4 @@
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.

-from .tokenization_bertweet import BertweetTokenizer
+from .tokenization_bertweet import BertweetTokenizer, BERT_TWEET_PRETRAINED_TOKENIZER_ARCHIVE_LIST
Author	SHA1	Message	Date
Thomas Wolf	4cec16ea28	mbart - marian - mobilebert - openai	2020-11-25 23:52:09 +01:00
Thomas Wolf	625496b1f7	longformer - lxmert	2020-11-25 19:30:33 +01:00
Thomas Wolf	52bb1bb40e	herbert and layoutlm	2020-11-25 19:14:55 +01:00
Thomas Wolf	d805add061	funnel and gpt2	2020-11-25 19:03:07 +01:00
Thomas Wolf	9b71c262de	flaubert - fsmt	2020-11-25 18:49:18 +01:00
Thomas Wolf	726ded8647	dpr and electra	2020-11-25 18:36:19 +01:00
Thomas Wolf	812f276272	ctrl - deberta - distilbert	2020-11-25 15:40:34 +01:00
Thomas Wolf	20fdaac34b	adding camembert	2020-11-25 13:21:45 +01:00
Thomas Wolf	0c0aefb23b	adding blenderbot - also support in auto	2020-11-25 13:14:17 +01:00
Thomas Wolf	aaf7ba4ec4	bert japanese and bert tweet	2020-11-25 12:42:10 +01:00
Thomas Wolf	824fae28cf	bert_generation	2020-11-25 12:09:05 +01:00
Thomas Wolf	d86b95aa86	clean-up	2020-11-25 00:20:58 +01:00
Thomas Wolf	b40ad98fc5	update bert tokenizer	2020-11-25 00:19:48 +01:00
Thomas Wolf	8c71c1b404	switch bart tokenizer	2020-11-24 18:23:25 +01:00
Thomas Wolf	dd50d6b932	switch Albert tokenizer to common hub access - fix autotokenizer reloading	2020-11-24 16:04:58 +01:00
Thomas Wolf	9876a9e06f	add tokenizer_class_name to tokenizers config	2020-11-24 00:05:26 +01:00