Add BridgeTower model (#20775)

* Commit with BTModel and latest HF code

* Placeholder classes for BTForMLM and BTForITR

* Importing Bert classes from transformers

* Removed objectives.py and dist_utils.py

* Removed swin_transformer.py

* Add image normalization, BridgeTowerForImageAndTextRetrieval

* Add center_crop

* Removing bert tokenizer and LCI references

* Tested config loading from HF transformers hub

* Removed state_dict updates and added path to hub

* Enable center crop

* Getting image_size from config, renaming num_heads and num_layers

* Handling max_length in BridgeTowerProcessor

* Add BridgeTowerForMaskedLM

* Add doc string for BridgeTowerConfig

* Add doc strings for BT config, processor, image processor

* Adding docs, removed swin

* Removed convert_bridgetower_original_to_pytorch.py

* Added doc files for bridgetower, removed is_vision

* Add support attention_mask=None and BridgeTowerModelOutput

* Fix formatting

* Fixes with 'make style', 'make quality', 'make fixup'

* Remove downstream tasks from BridgeTowerModel

* Formatting fixes, add return_dict to BT models

* Clean up after doc_test

* Update BTModelOutput return type, fix todo in doc

* Remove loss_names from init

* implement tests and update tuples returned by models

* Add image reference to bridgetower.mdx

* after make fix-copies, make fixup, make style, make quality, make repo-consistency

* Rename class names with BridgeTower prefix

* Fix for image_size in BTImageProcessor

* implement feature extraction bridgetower tests

* Update image_mean and image_std to be list

* remove unused import

* Removed old comments

* Rework CLIP

* update config in tests followed config update

* Formatting fixes

* Add copied from for BridgeTowerPredictionHeadTransform

* Update bridgetower.mdx

* Update test_feature_extraction_bridgetower.py

* Update bridgetower.mdx

* BridgeTowerForMaskedLM is conditioned on image too

* Add BridgeTowerForMaskedLM

* Fixes

* Call post_init to init weights

* Move freeze layers into method

* Remove BTFeatureExtractor, add BT under multimodal models

* Remove BTFeatureExtractor, add BT under multimodal models

* Code review feedback - cleanup

* Rename variables

* Formatting and style to PR review feedback

* Move center crop after resize

* Use named parameters

* Style fix for modeling_bridgetower.py

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

* Update src/transformers/models/bridgetower/modeling_bridgetower.py
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Rename config params, copy BERT classes, clean comments

* Cleanup irtr

* Replace Roberta imports, add BTTextConfig and Model

* Update docs, add visionconfig, consistent arg names

* make fixup

* Comments for forward in BTModel and make fixup

* correct tests

* Remove inconsistent roberta copied from

* Add BridgeTowerTextModel to dummy_pt_objects.py

* Add BridgeTowerTextModel to IGNORE_NON_TESTED

* Update docs for BT Text and Vision Configs

* Treat BridgeTowerTextModel as a private model

* BridgeTowerTextModel as private

* Run make fix-copies

* Adding BTTextModel to PRIVATE_MODELS

* Fix for issue with BT Text and Image configs

* make style changes

* Update README_ja.md

Add から to BridgeTower's description

* Clean up config, .mdx and arg names

* Fix init_weights. Remove nn.Sequential

* Formatting and style fixes

* Re-add tie_word_embeddings in config

* update test implementation

* update style

* remove commented out

* fix style

* Update README with abs for BridgeTower

* fix style

* fix mdx file

* Update bridgetower.mdx

* Update img src in bridgetower.mdx

* Update README.md

* Update README.md

* resolve style failed

* Update _toctree.yml

* Update README_ja.md

* Removed mlp_ratio, rename feats, rename BTCLIPModel

* Replace BTCLIP with BTVisionModel,pass in vision_config to BTVisionModel

* Add test_initialization support

* Add support for output_hidden_states

* Update support for output_hidden_states

* Add support for output_attentions

* Add docstring for output_hidden_states

* update tests

* add bridgetowervisionmodel as private model

* rerun the PR test

* Remove model_type, pass configs to classes, renames

* Change self.device to use weight device

* Remove image_size

* Style check fixes

* Add hidden_size and num_hidden_layers to BridgeTowerTransformer

* Update device setting

* cosmetic update

* trigger test again

* trigger tests again

* Update test_modeling_bridgetower.py

trigger tests again

* Update test_modeling_bridgetower.py

* minor update

* re-trigger tests

* Update docs/source/en/model_doc/bridgetower.mdx
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Remove pad, update max_text_len, doc cleanup, pass eps to LayerNorm

* Added copied to, some more review feedback

* make fixup

* Use BridgeTowerVisionEmbeddings

* Code cleanup

* Fixes for BridgeTowerVisionEmbeddings

* style checks

* re-tests

* fix embedding

* address comment on init file

* retrigger tests

* update import prepare_image_inputs

* update test_image_processing_bridgetower.py to reflect test_image_processing_common.py

* retrigger tests
Co-authored-by: Shaoyen Tseng <shao-yen.tseng@intel.com>
Co-authored-by: Tiep Le <tiep.le@intel.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>
Co-authored-by: Tiep Le <97980157+tileintel@users.noreply.github.com>

README.md

@@ -289,6 +289,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[BLIP](https://huggingface.co/docs/transformers/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/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.
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.

README_es.md

@@ -282,6 +282,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[BLIP](https://huggingface.co/docs/transformers/main/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/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.
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.

README_hd.md

@@ -254,6 +254,7 @@ conda install -c huggingface transformers
 1. **[BLIP](https://huggingface.co/docs/transformers/main/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigSicence Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (एलेक्सा से) कागज के साथ [बीईआरटी के लिए ऑप्टिमल सबआर्किटेक्चर एक्सट्रैक्शन](https://arxiv.org/abs/ 2010.10499) एड्रियन डी विंटर और डैनियल जे पेरी द्वारा।
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (हरबिन इंस्टिट्यूट ऑफ़ टेक्नोलॉजी/माइक्रोसॉफ्ट रिसर्च एशिया/इंटेल लैब्स से) कागज के साथ [ब्रिजटॉवर: विजन-लैंग्वेज रिप्रेजेंटेशन लर्निंग में एनकोडर्स के बीच ब्रिज बनाना](<https://arxiv.org/abs/2206.08657>) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (Google अनुसंधान से) साथ में कागज [ByT5: पूर्व-प्रशिक्षित बाइट-टू-बाइट मॉडल के साथ एक टोकन-मुक्त भविष्य की ओर] (https://arxiv.org/abs/2105.13626) Linting Xue, Aditya Barua, Noah Constant, रामी अल-रफू, शरण नारंग, मिहिर काले, एडम रॉबर्ट्स, कॉलिन रैफेल द्वारा पोस्ट किया गया।
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (इनरिया/फेसबुक/सोरबोन से) साथ में कागज [CamemBERT: एक टेस्टी फ्रेंच लैंग्वेज मॉडल](https:// arxiv.org/abs/1911.03894) लुई मार्टिन*, बेंजामिन मुलर*, पेड्रो जेवियर ऑर्टिज़ सुआरेज़*, योआन ड्यूपॉन्ट, लॉरेंट रोमरी, एरिक विलेमोन्टे डे ला क्लर्जरी, जैमे सेडाह और बेनोइट सगोट द्वारा।
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (Google रिसर्च से) साथ में दिया गया पेपर [कैनाइन: प्री-ट्रेनिंग ए एफिशिएंट टोकनाइजेशन-फ्री एनकोडर फॉर लैंग्वेज रिप्रेजेंटेशन]( https://arxiv.org/abs/2103.06874) जोनाथन एच क्लार्क, डैन गैरेट, यूलिया टर्क, जॉन विएटिंग द्वारा।

README_ja.md

@@ -316,6 +316,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[BLIP](https://huggingface.co/docs/transformers/main/model_doc/blip)** (Salesforce から) Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi から公開された研究論文: [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086)
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (BigScience workshop から) [BigScience Workshop](https://bigscience.huggingface.co/) から公開されました.
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (Alexa から) Adrian de Wynter and Daniel J. Perry から公開された研究論文: [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499)
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (Harbin Institute of Technology/Microsoft Research Asia/Intel Labs から) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (Google Research から) Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel から公開された研究論文: [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626)
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (Inria/Facebook/Sorbonne から) Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot から公開された研究論文: [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894)
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (Google Research から) Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting から公開された研究論文: [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874)

README_ko.md

@@ -231,6 +231,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[BLIP](https://huggingface.co/docs/transformers/main/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (Alexa 에서) Adrian de Wynter and Daniel J. Perry 의 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 논문과 함께 발표했습니다.
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (Google Research 에서) Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 의 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 논문과 함께 발표했습니다.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (Inria/Facebook/Sorbonne 에서) Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 의 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 논문과 함께 발표했습니다.
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (Google Research 에서) Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 의 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 논문과 함께 발표했습니다.

README_zh-hans.md

@@ -255,6 +255,7 @@ conda install -c huggingface transformers
 1. **[BLIP](https://huggingface.co/docs/transformers/main/model_doc/blip)** (来自 Salesforce) 伴随论文 [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) 由 Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi 发布。
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (来自 Alexa) 伴随论文 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 由 Adrian de Wynter and Daniel J. Perry 发布。
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (来自 Google Research) 伴随论文 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 由 Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 发布。
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (来自 Inria/Facebook/Sorbonne) 伴随论文 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 由 Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 发布。
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。

README_zh-hant.md

@@ -267,6 +267,7 @@ conda install -c huggingface transformers
 1. **[BLIP](https://huggingface.co/docs/transformers/main/model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[BridgeTower](https://huggingface.co/docs/transformers/main/model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/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.
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.

docs/source/en/_toctree.yml

@@ -516,6 +516,8 @@
         title: AltCLIP
       - local: model_doc/blip
         title: BLIP
+      - local: model_doc/bridgetower
+        title: BridgeTower
       - local: model_doc/chinese_clip
         title: Chinese-CLIP
       - local: model_doc/clip
@@ -595,4 +597,4 @@
     - local: internal/file_utils
       title: General Utilities
     title: Internal Helpers
-  title: API
\ No newline at end of file
+  title: API

docs/source/en/index.mdx

@@ -68,6 +68,7 @@ The documentation is organized into five sections:
 1. **[BLIP](model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
 1. **[BLOOM](model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
 1. **[BORT](model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
+1. **[BridgeTower](model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
 1. **[ByT5](model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[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.
 1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
@@ -250,6 +251,7 @@ Flax), PyTorch, and/or TensorFlow.
 |        BlenderbotSmall        |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             BLIP              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             BLOOM             |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          BridgeTower          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           CamemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            CANINE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         Chinese-CLIP          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/bridgetower.mdx

+<!--Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
+
+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.
+-->
+
+# BridgeTower
+
+## Overview
+
+The BridgeTower model was proposed in [BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The goal of this model is to build a
+bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder thus achieving remarkable performance on various downstream tasks with almost negligible additional performance and computational costs.
+
+This paper has been accepted to the [AAAI'23](https://aaai.org/Conferences/AAAI-23/) conference. 
+
+The abstract from the paper is the following:
+
+*Vision-Language (VL) models with the TWO-TOWER architecture have dominated visual-language representation learning in recent years.
+Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder.
+Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BRIDGETOWER, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the crossmodal encoder.
+This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BRIDGETOWER achieves state-of-the-art performance on various downstream vision-language tasks.
+In particular, on the VQAv2 test-std set, BRIDGETOWER achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs.
+Notably, when further scaling the model, BRIDGETOWER achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets.*
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/bridgetower_architecture%20.jpg"
+alt="drawing" width="600"/>
+
+<small> BridgeTower architecture. Taken from the <a href="https://arxiv.org/abs/2206.08657">original paper.</a> </small>
+
+## Usage
+
+BridgeTower consists of a visual encoder, a textual encoder and cross-modal encoder with multiple lightweight bridge layers.
+The goal of this approach was to build a bridge between each uni-modal encoder and the cross-modal encoder to enable comprehensive and detailed interaction at each layer of the cross-modal encoder.
+In principle, one can apply any visual, textual or cross-modal encoder in the proposed architecture.
+
+The [`BridgeTowerProcessor`] wraps [`RobertaTokenizer`] and [`BridgeTowerImageProcessor`] into a single instance to both
+encode the text and prepare the images respectively.
+
+The following example shows how to run image-text retrieval using [`BridgeTowerProcessor`] and [`BridgeTowerForImageAndTextRetrieval`].
+```python
+>>> from transformers import BridgeTowerProcessor, BridgeTowerForImageAndTextRetrieval
+>>> import requests
+>>> from PIL import Image
+
+>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+>>> texts = ["An image of two cats chilling on a couch", "A football player scoring a goal"]
+
+>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
+>>> model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
+
+>>> # forward pass
+>>> scores = dict()
+>>> for text in texts:
+...     # prepare inputs
+...     encoding = processor(image, text, return_tensors="pt")
+...     outputs = model(**encoding)
+...     scores[text] = outputs.logits[0, 1].item()
+```
+
+The following example shows how to run masked language modeling using [`BridgeTowerProcessor`] and [`BridgeTowerForMaskedLM`].
+
+```python
+>>> from transformers import BridgeTowerProcessor, BridgeTowerForMaskedLM
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "http://images.cocodataset.org/val2017/000000360943.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+>>> text = "a <mask> looking out of the window"
+
+>>> processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
+>>> model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm")
+
+>>> # prepare inputs
+>>> encoding = processor(image, text, return_tensors="pt")
+
+>>> # forward pass
+>>> outputs = model(**encoding)
+
+>>> results = processor.decode(outputs.logits.argmax(dim=-1).squeeze(0).tolist())
+
+>>> print(results)
+.a cat looking out of the window.
+```
+
+This model was contributed by [Anahita Bhiwandiwalla](https://huggingface.co/anahita-b), [Tiep Le](https://huggingface.co/Tile) and [Shaoyen Tseng](https://huggingface.co/shaoyent). The original code can be found [here](https://github.com/microsoft/BridgeTower).
+
+
+Tips:
+
+- This implementation of BridgeTower uses [`RobertaTokenizer`] to generate text embeddings and OpenAI's CLIP/ViT model to compute visual embeddings.
+- Checkpoints for pre-trained [bridgeTower-base](https://huggingface.co/BridgeTower/bridgetower-base) and [bridgetower masked language modeling and image text matching](https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm) are released.
+- Please refer to [Table 5](https://arxiv.org/pdf/2206.08657.pdf) for BridgeTower's performance on Image Retrieval and other down stream tasks.
+- The PyTorch version of this model is only available in torch 1.10 and higher.
+
+
+## BridgeTowerConfig
+
+[[autodoc]] BridgeTowerConfig
+
+## BridgeTowerTextConfig
+
+[[autodoc]] BridgeTowerTextConfig
+
+## BridgeTowerVisionConfig
+
+[[autodoc]] BridgeTowerVisionConfig
+
+## BridgeTowerImageProcessor
+
+[[autodoc]] BridgeTowerImageProcessor
+    - preprocess
+
+## BridgeTowerProcessor
+
+[[autodoc]] BridgeTowerProcessor
+    - __call__
+
+## BridgeTowerModel
+
+[[autodoc]] BridgeTowerModel
+    - forward
+
+## BridgeTowerForMaskedLM
+
+[[autodoc]] BridgeTowerForMaskedLM
+    - forward
+
+## BridgeTowerForImageAndTextRetrieval
+
+[[autodoc]] BridgeTowerForImageAndTextRetrieval
+    - forward
+

src/transformers/__init__.py

@@ -185,6 +185,13 @@ _import_structure = {
     ],
     "models.bloom": ["BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP", "BloomConfig"],
     "models.bort": [],
+    "models.bridgetower": [
+        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "BridgeTowerConfig",
+        "BridgeTowerProcessor",
+        "BridgeTowerTextConfig",
+        "BridgeTowerVisionConfig",
+    ],
     "models.byt5": ["ByT5Tokenizer"],
     "models.camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig"],
     "models.canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig", "CanineTokenizer"],
@@ -779,6 +786,7 @@ else:
     _import_structure["models.beit"].extend(["BeitFeatureExtractor", "BeitImageProcessor"])
     _import_structure["models.bit"].extend(["BitImageProcessor"])
     _import_structure["models.blip"].extend(["BlipImageProcessor"])
+    _import_structure["models.bridgetower"].append("BridgeTowerImageProcessor")
     _import_structure["models.chinese_clip"].extend(["ChineseCLIPFeatureExtractor", "ChineseCLIPImageProcessor"])
     _import_structure["models.clip"].extend(["CLIPFeatureExtractor", "CLIPImageProcessor"])
     _import_structure["models.conditional_detr"].extend(
@@ -1155,6 +1163,15 @@ else:
             "BloomPreTrainedModel",
         ]
     )
+    _import_structure["models.bridgetower"].extend(
+        [
+            "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "BridgeTowerForImageAndTextRetrieval",
+            "BridgeTowerForMaskedLM",
+            "BridgeTowerModel",
+            "BridgeTowerPreTrainedModel",
+        ]
+    )
     _import_structure["models.camembert"].extend(
         [
             "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3611,6 +3628,13 @@ if TYPE_CHECKING:
         BlipVisionConfig,
     )
     from .models.bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig
+    from .models.bridgetower import (
+        BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        BridgeTowerConfig,
+        BridgeTowerProcessor,
+        BridgeTowerTextConfig,
+        BridgeTowerVisionConfig,
+    )
     from .models.byt5 import ByT5Tokenizer
     from .models.camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
     from .models.canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig, CanineTokenizer
@@ -4137,6 +4161,7 @@ if TYPE_CHECKING:
         from .models.beit import BeitFeatureExtractor, BeitImageProcessor
         from .models.bit import BitImageProcessor
         from .models.blip import BlipImageProcessor
+        from .models.bridgetower import BridgeTowerImageProcessor
         from .models.chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
         from .models.clip import CLIPFeatureExtractor, CLIPImageProcessor
         from .models.conditional_detr import ConditionalDetrFeatureExtractor, ConditionalDetrImageProcessor
@@ -4456,6 +4481,13 @@ if TYPE_CHECKING:
             BloomModel,
             BloomPreTrainedModel,
         )
+        from .models.bridgetower import (
+            BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            BridgeTowerForImageAndTextRetrieval,
+            BridgeTowerForMaskedLM,
+            BridgeTowerModel,
+            BridgeTowerPreTrainedModel,
+        )
         from .models.camembert import (
             CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             CamembertForCausalLM,

src/transformers/models/__init__.py

@@ -38,6 +38,7 @@ from . import (
     blip,
     bloom,
     bort,
+    bridgetower,
     byt5,
     camembert,
     canine,

src/transformers/models/auto/configuration_auto.py

@@ -44,6 +44,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
         ("blenderbot-small", "BlenderbotSmallConfig"),
         ("blip", "BlipConfig"),
         ("bloom", "BloomConfig"),
+        ("bridgetower", "BridgeTowerConfig"),
         ("camembert", "CamembertConfig"),
         ("canine", "CanineConfig"),
         ("chinese_clip", "ChineseCLIPConfig"),
@@ -210,6 +211,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
         ("blenderbot-small", "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("blip", "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("bloom", "BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP"),
+        ("bridgetower", "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("camembert", "CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("canine", "CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP"),
         ("chinese_clip", "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -365,6 +367,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
         ("blip", "BLIP"),
         ("bloom", "BLOOM"),
         ("bort", "BORT"),
+        ("bridgetower", "BridgeTower"),
         ("byt5", "ByT5"),
         ("camembert", "CamemBERT"),
         ("canine", "CANINE"),

src/transformers/models/auto/image_processing_auto.py

@@ -40,6 +40,7 @@ IMAGE_PROCESSOR_MAPPING_NAMES = OrderedDict(
         ("beit", "BeitImageProcessor"),
         ("bit", "BitImageProcessor"),
         ("blip", "BlipImageProcessor"),
+        ("bridgetower", "BridgeTowerImageProcessor"),
         ("chinese_clip", "ChineseCLIPImageProcessor"),
         ("clip", "CLIPImageProcessor"),
         ("clipseg", "ViTImageProcessor"),

src/transformers/models/auto/modeling_auto.py

@@ -43,6 +43,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
         ("blenderbot-small", "BlenderbotSmallModel"),
         ("blip", "BlipModel"),
         ("bloom", "BloomModel"),
+        ("bridgetower", "BridgeTowerModel"),
         ("camembert", "CamembertModel"),
         ("canine", "CanineModel"),
         ("chinese_clip", "ChineseCLIPModel"),

src/transformers/models/auto/processing_auto.py

@@ -43,6 +43,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
     [
         ("altclip", "AltCLIPProcessor"),
         ("blip", "BLIPProcessor"),
+        ("bridgetower", "BridgeTowerProcessor"),
         ("chinese_clip", "ChineseCLIPProcessor"),
         ("clip", "CLIPProcessor"),
         ("clipseg", "CLIPSegProcessor"),

src/transformers/models/auto/tokenization_auto.py

@@ -79,6 +79,7 @@ else:
             ("blenderbot-small", ("BlenderbotSmallTokenizer", None)),
             ("blip", ("BertTokenizer", "BertTokenizerFast" if is_tokenizers_available() else None)),
             ("bloom", (None, "BloomTokenizerFast" if is_tokenizers_available() else None)),
+            ("bridgetower", ("RobertaTokenizer", "RobertaTokenizerFast" if is_tokenizers_available() else None)),
             ("byt5", ("ByT5Tokenizer", None)),
             (
                 "camembert",

src/transformers/models/bridgetower/__init__.py

+# 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.
+
+# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+from typing import TYPE_CHECKING
+
+# rely on isort to merge the imports
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
+
+
+_import_structure = {
+    "configuration_bridgetower": [
+        "BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP",
+        "BridgeTowerConfig",
+        "BridgeTowerTextConfig",
+        "BridgeTowerVisionConfig",
+    ],
+    "processing_bridgetower": ["BridgeTowerProcessor"],
+}
+
+try:
+    if not is_vision_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["image_processing_bridgetower"] = ["BridgeTowerImageProcessor"]
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_bridgetower"] = [
+        "BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "BridgeTowerForImageAndTextRetrieval",
+        "BridgeTowerForMaskedLM",
+        "BridgeTowerModel",
+        "BridgeTowerPreTrainedModel",
+    ]
+
+
+if TYPE_CHECKING:
+    from .configuration_bridgetower import (
+        BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
+        BridgeTowerConfig,
+        BridgeTowerTextConfig,
+        BridgeTowerVisionConfig,
+    )
+    from .processing_bridgetower import BridgeTowerProcessor
+
+    try:
+        if not is_vision_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .image_processing_bridgetower import BridgeTowerImageProcessor
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_bridgetower import (
+            BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
+            BridgeTowerForImageAndTextRetrieval,
+            BridgeTowerForMaskedLM,
+            BridgeTowerModel,
+            BridgeTowerPreTrainedModel,
+        )
+
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)

src/transformers/models/bridgetower/configuration_bridgetower.py

+# coding=utf-8
+# Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. team. All rights reserved.
+#
+# 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.
+""" BridgeTower model configuration"""
+
+import copy
+import os
+from typing import Union
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "BridgeTower/bridgetower-base": "https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json",
+    "BridgeTower/bridgetower-base-itm-mlm": (
+        "https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json"
+    ),
+}
+
+
+class BridgeTowerVisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the vision configuration of a [`BridgeTowerModel`]. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the bridgetower-base
+    [BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in visual encoder model.
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        image_size (`int`, *optional*, defaults to 288):
+            The size (resolution) of each image.
+        initializer_factor (`float``, *optional*, defaults to 1):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
+        stop_gradient (`bool`, *optional*, defaults to `False`):
+            Whether to stop gradient for training.
+        share_layernorm (`bool`, *optional*, defaults to `True`):
+            Whether LayerNorm layers are shared.
+        remove_last_layer (`bool`, *optional*, defaults to `False`):
+            Whether to remove the last layer from the vision encoder.
+
+
+    Example:
+
+    ```python
+    >>> from transformers import BridgeTowerVisionConfig
+
+    >>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration for the vision model
+    >>> configuration = BridgeTowerVisionConfig()
+
+    >>> # Accessing the configuration
+    >>> configuration
+    ```"""
+    model_type = "bridgetower_vision_model"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_channels=3,
+        patch_size=16,
+        image_size=288,
+        initializer_factor=1,
+        layer_norm_eps=1e-05,
+        stop_gradient=False,
+        share_layernorm=True,
+        remove_last_layer=False,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.initializer_factor = initializer_factor
+        self.layer_norm_eps = layer_norm_eps
+        self.stop_gradient = stop_gradient
+        self.share_layernorm = share_layernorm
+        self.remove_last_layer = remove_last_layer
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if config_dict.get("model_type") == "bridgetower":
+            config_dict = config_dict["text_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class BridgeTowerTextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the text configuration of a [`BridgeTowerModel`]. The default values here
+    are copied from RoBERTa. Instantiating a configuration with the defaults will yield a similar configuration to that
+    of the bridgetower-base [BridegTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/)
+    architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50265):
+            Vocabulary size of the text part of the model. Defines the number of different tokens that can be
+            represented by the `inputs_ids` passed when calling [`BridgeTowerModel`].
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
+        hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"silu"` and `"gelu_new"` are supported.
+        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (`int`, *optional*, defaults to 514):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (`int`, *optional*, defaults to 2):
+            The vocabulary size of the `token_type_ids`.
+        initializer_factor (`float``, *optional*, defaults to 1):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
+        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
+            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
+            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
+            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
+            For more information on `"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).
+        is_decoder (`bool`, *optional*, defaults to `False`):
+            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        classifier_dropout (`float`, *optional*):
+            The dropout ratio for the classification head.
+
+    Example:
+
+    ```python
+    >>> from transformers import BridgeTowerTextConfig
+
+    >>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration for the text model
+    >>> configuration = BridgeTowerTextConfig()
+
+    >>> # Accessing the configuration
+    >>> configuration
+    ```"""
+    model_type = "bridgetower_text_model"
+
+    def __init__(
+        self,
+        vocab_size=50265,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        initializer_factor=1,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=514,
+        type_vocab_size=1,
+        initializer_range=0.02,
+        layer_norm_eps=1e-05,
+        pad_token_id=1,
+        bos_token_id=0,
+        eos_token_id=2,
+        position_embedding_type="absolute",
+        use_cache=True,
+        classifier_dropout=None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.hidden_act = hidden_act
+        self.initializer_factor = initializer_factor
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.position_embedding_type = position_embedding_type
+        self.use_cache = use_cache
+        self.classifier_dropout = classifier_dropout
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
+        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
+
+        if config_dict.get("model_type") == "bridgetower":
+            config_dict = config_dict["text_config"]
+
+        if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type:
+            logger.warning(
+                f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
+                f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."
+            )
+
+        return cls.from_dict(config_dict, **kwargs)
+
+
+class BridgeTowerConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BridgeTowerModel`]. It is used to instantiate a
+    BridgeTower model according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the bridgetower-base
+    [BridgeTower/bridgetower-base](https://huggingface.co/BridgeTower/bridgetower-base/) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        share_cross_modal_transformer_layers (`bool`, *optional*, defaults to `True`):
+            Whether cross modal transformer layers are shared.
+        drop_rate (`float`, *optional*, defaults to 0.1):
+            Drop out probability.
+        head_hidden_scale (`int`, *optional*, defaults to 2):
+            Scale of hidden layers head.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler.
+        hidden_size (`int`, *optional*, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        initializer_factor (`float``, *optional*, defaults to 1):
+            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
+            testing).
+        is_encoder_decoder (`bool`, *optional*, defaults to `False`):
+            Whether this is an encoder/decoder model
+        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
+        share_link_tower_layers (`bool`, *optional*, defaults to `False`):
+            Whether the bride/link tower layers are shared.
+        link_tower_type (`str`, *optional*, defaults to `"add"`):
+            Type of the bridge/link layer.
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 6):
+            Number of hidden layers in the Transformer encoder.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie input and output embeddings.
+        init_layernorm_from_vision_encoder (`bool`, *optional*, defaults to `False`):
+            Whether to init LayerNorm from the vision encoder.
+        text_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`BridgeTowerTextConfig`].
+        vision_config (`dict`, *optional*):
+            Dictionary of configuration options used to initialize [`BridgeTowerVisionConfig`].
+
+    Example:
+
+    ```python
+    >>> from transformers import BridgeTowerModel, BridgeTowerConfig
+
+    >>> # Initializing a BridgeTower BridgeTower/bridgetower-base style configuration
+    >>> configuration = BridgeTowerConfig()
+
+    >>> # Initializing a model from the BridgeTower/bridgetower-base style configuration
+    >>> model = BridgeTowerModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "bridgetower"
+
+    def __init__(
+        self,
+        share_cross_modal_transformer_layers=True,
+        drop_rate=0.1,
+        head_hidden_scale=2,
+        hidden_act="gelu",
+        hidden_size=768,
+        initializer_factor=1,
+        is_encoder_decoder=False,
+        layer_norm_eps=1e-05,
+        share_link_tower_layers=False,
+        link_tower_type="add",
+        num_attention_heads=12,
+        num_hidden_layers=6,
+        tie_word_embeddings=False,
+        init_layernorm_from_vision_encoder=False,
+        text_config=None,
+        vision_config=None,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers
+        self.drop_rate = drop_rate
+        self.head_hidden_scale = head_hidden_scale
+        self.hidden_act = hidden_act
+        self.hidden_size = hidden_size
+        self.initializer_factor = initializer_factor
+        self.is_encoder_decoder = is_encoder_decoder
+        self.layer_norm_eps = layer_norm_eps
+        self.share_link_tower_layers = share_link_tower_layers
+        self.link_tower_type = link_tower_type
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.tie_word_embeddings = tie_word_embeddings
+        self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder
+
+        text_config_dict = kwargs.pop("text_config_dict", None)
+        vision_config_dict = kwargs.pop("vision_config_dict", None)
+        if text_config_dict is not None:
+            text_config = text_config_dict
+        if vision_config_dict is not None:
+            vision_config = vision_config_dict
+
+        if text_config is None:
+            text_config = {}
+            logger.info("text_config is None. Initializing the BridgeTowerTextConfig with default values.")
+
+        if vision_config is None:
+            vision_config = {}
+            logger.info("vision_config is None. Initializing the BridgeTowerVisionConfig with default values.")
+
+        self.text_config = BridgeTowerTextConfig(**text_config)
+        self.vision_config = BridgeTowerVisionConfig(**vision_config)
+
+    @classmethod
+    def from_text_vision_configs(
+        cls, text_config: BridgeTowerTextConfig, vision_config: BridgeTowerVisionConfig, **kwargs
+    ):
+        r"""
+        Instantiate a [`BridgeTowerConfig`] (or a derived class) from BridgeTower text model configuration. Returns:
+            [`BridgeTowerConfig`]: An instance of a configuration object
+        """
+
+        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)
+
+    def to_dict(self):
+        """
+        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].
+
+        Returns:
+            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
+        """
+        output = copy.deepcopy(self.__dict__)
+        output["text_config"] = self.text_config.to_dict()
+        output["vision_config"] = self.vision_config.to_dict()
+        output["model_type"] = self.__class__.model_type
+        return output