Add OWL-ViT model for zero-shot object detection (#17938)

* add owlvit model skeleton * add class and box predictor heads * convert modified flax clip to pytorch * fix box and class predictors * add OwlViTImageTextEmbedder * convert class and box head checkpoints * convert image text embedder checkpoints * add object detection head * fix bugs * update conversion script * update conversion script * fix q,v,k,out weight conversion conversion * add owlvit object detection output * fix bug in image embedder * fix bugs in text embedder * fix positional embeddings * fix bug in inference mode vision pooling * update docs, init tokenizer and processor files * support batch processing * add OwlViTProcessor * remove merge conflicts * readd owlvit imports * fix bug in OwlViTProcessor imports * fix bugs in processor * update docs * fix bugs in processor * update owlvit docs * add OwlViTFeatureExtractor * style changes, add postprocess method to feature extractor * add feature extractor and processor tests * add object detection tests * update conversion script * update config paths * update config paths * fix configuration paths and bugs * fix bugs in OwlViT tests * add import checks to processor * fix docs and minor issues * fix docs and minor issues * fix bugs and issues * fix bugs and issues * fix bugs and issues * fix bugs and issues * update docs and examples * fix bugs and issues * update conversion script, fix positional embeddings * process 2D input ids, update tests * fix style and quality issues * update docs * update docs and imports * update OWL-ViT index.md * fix bug in OwlViT feature ext tests * fix code examples, return_dict by default * return_dict by default * minor fixes, add tests to processor * small fixes * add output_attentions arg to main model * fix bugs * remove output_hidden_states arg from main model * update self.config variables * add option to return last_hidden_states * fix bug in config variables * fix copied from statements * fix small issues and bugs * fix bugs * fix bugs, support greyscale images * run fixup * update repo name * merge OwlViTImageTextEmbedder with obj detection head * fix merge conflict * fix merge conflict * make fixup * fix bugs * fix bugs * add additional processor test
2022-07-22 13:35:32 +03:00
parent 99eb9b523f
commit 12d66b4701
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--- a/README.md
+++ b/README.md
@@ -332,6 +332,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[NLLB](https://huggingface.co/docs/transformers/main/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/main/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[Pegasus](https://huggingface.co/docs/transformers/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.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
--- a/README_ko.md
+++ b/README_ko.md
@@ -288,6 +288,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[NLLB](https://huggingface.co/docs/transformers/main/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/main/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[Pegasus](https://huggingface.co/docs/transformers/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.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
--- a/README_zh-hans.md
+++ b/README_zh-hans.md
@@ -312,6 +312,7 @@ conda install -c huggingface transformers
 1. **[NLLB](https://huggingface.co/docs/transformers/main/model_doc/nllb)** (来自 Meta) 伴随论文 [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) 由 the NLLB team 发布。
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (来自 the University of Wisconsin - Madison) 伴随论文 [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) 由 Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh 发布。
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (来自 Meta AI) 伴随论文 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 由 Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 发布。
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/main/model_doc/owlvit)** (来自 Google AI) 伴随论文 [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) 由 Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby 发布。
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (来自 VinAI Research) 伴随论文 [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) 由 Dat Quoc Nguyen and Anh Tuan Nguyen 发布。
--- a/README_zh-hant.md
+++ b/README_zh-hant.md
@@ -324,6 +324,7 @@ conda install -c huggingface transformers
 1. **[NLLB](https://huggingface.co/docs/transformers/main/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/main/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[Pegasus](https://huggingface.co/docs/transformers/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.
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -326,6 +326,8 @@
      title: Nyströmformer
    - local: model_doc/opt
      title: OPT
    - local: model_doc/owlvit
      title: OWL-ViT
    - local: model_doc/pegasus
      title: Pegasus
    - local: model_doc/perceiver
--- a/docs/source/en/index.mdx
+++ b/docs/source/en/index.mdx
@@ -130,6 +130,7 @@ The library currently contains JAX, PyTorch and TensorFlow implementations, pret
 1. **[NLLB](model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
 1. **[Nyströmformer](model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
 1. **[OPT](master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[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.
 1. **[Perceiver IO](model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
@@ -263,6 +264,7 @@ Flax), PyTorch, and/or TensorFlow.
 |         OpenAI GPT          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |        OpenAI GPT-2         |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             OPT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           OWL-ViT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           Pegasus           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          Perceiver          |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           PLBart            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
--- a/docs/source/en/model_doc/owlvit.mdx
+++ b/docs/source/en/model_doc/owlvit.mdx
@@ -0,0 +1,101 @@
 <!--Copyright 2022 The HuggingFace Team. All rights reserved.
 Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
 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.
 -->
 # OWL-ViT
 ## Overview
 The OWL-ViT (short for Vision Transformer for Open-World Localization) was proposed in [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby. OWL-ViT is an open-vocabulary object detection network trained on a variety of (image, text) pairs. It can be used to query an image with one or multiple text queries to search for and detect target objects described in text.
 The abstract from the paper is the following:
 *Combining simple architectures with large-scale pre-training has led to massive improvements in image classification. For object detection, pre-training and scaling approaches are less well established, especially in the long-tailed and open-vocabulary setting, where training data is relatively scarce. In this paper, we propose a strong recipe for transferring image-text models to open-vocabulary object detection. We use a standard Vision Transformer architecture with minimal modifications, contrastive image-text pre-training, and end-to-end detection fine-tuning. Our analysis of the scaling properties of this setup shows that increasing image-level pre-training and model size yield consistent improvements on the downstream detection task. We provide the adaptation strategies and regularizations needed to attain very strong performance on zero-shot text-conditioned and one-shot image-conditioned object detection. Code and models are available on GitHub.*
 ## Usage
 OWL-ViT is a zero-shot text-conditioned object detection model. OWL-ViT uses [CLIP](clip) as its multi-modal backbone, with a ViT-like Transformer to get visual features and a causal language model to get the text features. To use CLIP for detection, OWL-ViT removes the final token pooling layer of the vision model and attaches a lightweight classification and box head to each transformer output token. Open-vocabulary classification is enabled by replacing the fixed classification layer weights with the class-name embeddings obtained from the text model. The authors first train CLIP from scratch and fine-tune it end-to-end with the classification and box heads on standard detection datasets using a bipartite matching loss. One or multiple text queries per image can be used to perform zero-shot text-conditioned object detection. 
 [`OwlViTFeatureExtractor`] can be used to resize (or rescale) and normalize images for the model and [`CLIPTokenizer`] is used to encode the text. [`OwlViTProcessor`] wraps [`OwlViTFeatureExtractor`] and [`CLIPTokenizer`] into a single instance to both encode the text and prepare the images. The following example shows how to perform object detection using [`OwlViTProcessor`] and [`OwlViTForObjectDetection`].
 ```python
 >>> import requests
 >>> from PIL import Image
 >>> import torch
 >>> from transformers import OwlViTProcessor, OwlViTForObjectDetection
 >>> processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
 >>> model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32")
 >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 >>> image = Image.open(requests.get(url, stream=True).raw)
 >>> inputs = processor(text=[["a photo of a cat", "a photo of a dog"]], images=image, return_tensors="pt")
 >>> outputs = model(**inputs)
 >>> logits = outputs["logits"]  # Prediction logits of shape [batch_size, num_patches, num_max_text_queries]
 >>> boxes = outputs["pred_boxes"]  # Object box boundaries of shape [batch_size, num_patches, 4]
 >>> batch_size = boxes.shape[0]
 >>> for i in range(batch_size):  # Loop over sets of images and text queries
 ...     boxes = outputs["pred_boxes"][i]
 ...     logits = torch.max(outputs["logits"][i], dim=-1)
 ...     scores = torch.sigmoid(logits.values)
 ...     labels = logits.indices
 ```
 This model was contributed by [adirik](https://huggingface.co/adirik). The original code can be found [here](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit).
 ## OwlViTConfig
 [[autodoc]] OwlViTConfig
    - from_text_vision_configs
 ## OwlViTTextConfig
 [[autodoc]] OwlViTTextConfig
 ## OwlViTVisionConfig
 [[autodoc]] OwlViTVisionConfig
 ## OwlViTFeatureExtractor
 [[autodoc]] OwlViTFeatureExtractor
    - __call__
 ## OwlViTProcessor
 [[autodoc]] OwlViTProcessor
 ## OwlViTModel
 [[autodoc]] OwlViTModel
    - forward
    - get_text_features
    - get_image_features
 ## OwlViTTextModel
 [[autodoc]] OwlViTTextModel
    - forward
 ## OwlViTVisionModel
 [[autodoc]] OwlViTVisionModel
    - forward
 ## OwlViTForObjectDetection
 [[autodoc]] OwlViTForObjectDetection
    - forward
--- a/src/transformers/init.py
+++ b/src/transformers/init.py
@@ -273,6 +273,13 @@ _import_structure = {
    ],
    "models.openai": ["OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenAIGPTConfig", "OpenAIGPTTokenizer"],
    "models.opt": ["OPTConfig"],
    "models.owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTProcessor",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "models.pegasus": ["PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusConfig", "PegasusTokenizer"],
    "models.perceiver": ["PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP", "PerceiverConfig", "PerceiverTokenizer"],
    "models.phobert": ["PhobertTokenizer"],
@@ -641,6 +648,7 @@ else:
    _import_structure["models.levit"].append("LevitFeatureExtractor")
    _import_structure["models.maskformer"].append("MaskFormerFeatureExtractor")
    _import_structure["models.mobilevit"].append("MobileViTFeatureExtractor")
    _import_structure["models.owlvit"].append("OwlViTFeatureExtractor")
    _import_structure["models.perceiver"].append("PerceiverFeatureExtractor")
    _import_structure["models.poolformer"].append("PoolFormerFeatureExtractor")
    _import_structure["models.segformer"].append("SegformerFeatureExtractor")
@@ -1507,6 +1515,16 @@ else:
            "OPTForSequenceClassification",
        ]
    )
    _import_structure["models.owlvit"].extend(
        [
            "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
            "OwlViTModel",
            "OwlViTPreTrainedModel",
            "OwlViTTextModel",
            "OwlViTVisionModel",
            "OwlViTForObjectDetection",
        ]
    )
    _import_structure["models.pegasus"].extend(
        ["PegasusForCausalLM", "PegasusForConditionalGeneration", "PegasusModel", "PegasusPreTrainedModel"]
    )
@@ -3012,6 +3030,13 @@ if TYPE_CHECKING:
    from .models.nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
    from .models.openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig, OpenAIGPTTokenizer
    from .models.opt import OPTConfig
    from .models.owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTProcessor,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .models.pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig, PegasusTokenizer
    from .models.perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverTokenizer
    from .models.phobert import PhobertTokenizer
@@ -3328,6 +3353,7 @@ if TYPE_CHECKING:
        from .models.levit import LevitFeatureExtractor
        from .models.maskformer import MaskFormerFeatureExtractor
        from .models.mobilevit import MobileViTFeatureExtractor
        from .models.owlvit import OwlViTFeatureExtractor
        from .models.perceiver import PerceiverFeatureExtractor
        from .models.poolformer import PoolFormerFeatureExtractor
        from .models.segformer import SegformerFeatureExtractor
@@ -4044,6 +4070,14 @@ if TYPE_CHECKING:
            OPTModel,
            OPTPreTrainedModel,
        )
        from .models.owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )
        from .models.pegasus import (
            PegasusForCausalLM,
            PegasusForConditionalGeneration,
--- a/src/transformers/models/init.py
+++ b/src/transformers/models/init.py
@@ -101,6 +101,7 @@ from . import (
    nystromformer,
    openai,
    opt,
    owlvit,
    pegasus,
    perceiver,
    phobert,
--- a/src/transformers/models/auto/configuration_auto.py
+++ b/src/transformers/models/auto/configuration_auto.py
@@ -98,6 +98,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
        ("nystromformer", "NystromformerConfig"),
        ("openai-gpt", "OpenAIGPTConfig"),
        ("opt", "OPTConfig"),
        ("owlvit", "OwlViTConfig"),
        ("pegasus", "PegasusConfig"),
        ("perceiver", "PerceiverConfig"),
        ("plbart", "PLBartConfig"),
@@ -216,6 +217,7 @@ CONFIG_ARCHIVE_MAP_MAPPING_NAMES = OrderedDict(
        ("nystromformer", "NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("openai-gpt", "OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("opt", "OPT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("owlvit", "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("pegasus", "PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("perceiver", "PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP"),
        ("plbart", "PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP"),
@@ -346,6 +348,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
        ("nystromformer", "Nyströmformer"),
        ("openai-gpt", "OpenAI GPT"),
        ("opt", "OPT"),
        ("owlvit", "OWL-ViT"),
        ("pegasus", "Pegasus"),
        ("perceiver", "Perceiver"),
        ("phobert", "PhoBERT"),
--- a/src/transformers/models/auto/feature_extraction_auto.py
+++ b/src/transformers/models/auto/feature_extraction_auto.py
@@ -58,6 +58,7 @@ FEATURE_EXTRACTOR_MAPPING_NAMES = OrderedDict(
        ("maskformer", "MaskFormerFeatureExtractor"),
        ("mctct", "MCTCTFeatureExtractor"),
        ("mobilevit", "MobileViTFeatureExtractor"),
        ("owlvit", "OwlViTFeatureExtractor"),
        ("perceiver", "PerceiverFeatureExtractor"),
        ("poolformer", "PoolFormerFeatureExtractor"),
        ("regnet", "ConvNextFeatureExtractor"),
--- a/src/transformers/models/auto/modeling_auto.py
+++ b/src/transformers/models/auto/modeling_auto.py
@@ -98,6 +98,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
        ("nystromformer", "NystromformerModel"),
        ("openai-gpt", "OpenAIGPTModel"),
        ("opt", "OPTModel"),
        ("owlvit", "OwlViTModel"),
        ("pegasus", "PegasusModel"),
        ("perceiver", "PerceiverModel"),
        ("plbart", "PLBartModel"),
--- a/src/transformers/models/auto/processing_auto.py
+++ b/src/transformers/models/auto/processing_auto.py
@@ -43,6 +43,7 @@ PROCESSOR_MAPPING_NAMES = OrderedDict(
        ("layoutlmv2", "LayoutLMv2Processor"),
        ("layoutlmv3", "LayoutLMv3Processor"),
        ("layoutxlm", "LayoutXLMProcessor"),
        ("owlvit", "OwlViTProcessor"),
        ("sew", "Wav2Vec2Processor"),
        ("sew-d", "Wav2Vec2Processor"),
        ("speech_to_text", "Speech2TextProcessor"),
--- a/src/transformers/models/auto/tokenization_auto.py
+++ b/src/transformers/models/auto/tokenization_auto.py
@@ -193,6 +193,7 @@ else:
            ),
            ("openai-gpt", ("OpenAIGPTTokenizer", "OpenAIGPTTokenizerFast" if is_tokenizers_available() else None)),
            ("opt", ("GPT2Tokenizer", None)),
            ("owlvit", ("CLIPTokenizer", "CLIPTokenizerFast" if is_tokenizers_available() else None)),
            (
                "pegasus",
                (
--- a/src/transformers/models/clip/configuration_clip.py
+++ b/src/transformers/models/clip/configuration_clip.py
@@ -199,6 +199,7 @@ class CLIPVisionConfig(PretrainedConfig):
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        patch_size=32,
        hidden_act="quick_gelu",
@@ -216,6 +217,7 @@ class CLIPVisionConfig(PretrainedConfig):
        self.dropout = dropout
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.image_size = image_size
        self.initializer_range = initializer_range
--- a/src/transformers/models/owlvit/init.py
+++ b/src/transformers/models/owlvit/init.py
@@ -0,0 +1,100 @@
 # 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 2022 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with 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
 from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
 )
 _import_structure = {
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
 }
 try:
    if not is_vision_available() or not is_torch_available():
        raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
    pass
 else:
    _import_structure["feature_extraction_owlvit"] = ["OwlViTFeatureExtractor"]
 try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
    pass
 else:
    _import_structure["modeling_owlvit"] = [
        "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "OwlViTModel",
        "OwlViTPreTrainedModel",
        "OwlViTTextModel",
        "OwlViTVisionModel",
        "OwlViTForObjectDetection",
    ]
 if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor
    try:
        if not is_vision_available() or not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )
 else:
    import sys
    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
--- a/src/transformers/models/owlvit/configuration_owlvit.py
+++ b/src/transformers/models/owlvit/configuration_owlvit.py
@@ -0,0 +1,336 @@
 # coding=utf-8
 # Copyright 2022 The 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.
 """ OWL-ViT model configuration"""
 import copy
 import os
 from typing import Dict, Union
 from ...configuration_utils import PretrainedConfig
 from ...utils import logging
 logger = logging.get_logger(__name__)
 OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
 }
 class OwlViTTextConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
    OwlViT text encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the OwlViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 49408):
            Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`OwlViTTextModel`].
        hidden_size (`int`, *optional*, defaults to 512):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2048):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        max_position_embeddings (`int`, *optional*, defaults to 16):
            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).
        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*,
            defaults to 1e-5): The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_factor (`float`, *optional*, defaults to 1):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
    Example:
    ```python
    >>> from transformers import OwlViTTextConfig, OwlViTTextModel
    >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
    >>> configuration = OwlViTTextConfig()
    >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
    >>> model = OwlViTTextModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "owlvit_text_model"
    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        num_hidden_layers=12,
        num_attention_heads=8,
        max_position_embeddings=16,
        hidden_act="quick_gelu",
        layer_norm_eps=0.00001,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        pad_token_id=0,
        bos_token_id=49406,
        eos_token_id=49407,
        **kwargs
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.max_position_embeddings = max_position_embeddings
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
    @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)
        # get the text config dict if we are loading from OwlViTConfig
        if config_dict.get("model_type") == "owlvit":
            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 OwlViTVisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
    an OWL-ViT image encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the OWL-ViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        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.
        image_size (`int`, *optional*, defaults to 768):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 32):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"quick_gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*,
            defaults to 1e-5): The epsilon used by the layer normalization layers.
        dropout (`float`, *optional*, defaults to 0.0):
            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        initializer_factor (`float``, *optional*, defaults to 1):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
    Example:
    ```python
    >>> from transformers import OwlViTVisionConfig, OwlViTVisionModel
    >>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration
    >>> configuration = OwlViTVisionConfig()
    >>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration
    >>> model = OwlViTVisionModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "owlvit_vision_model"
    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        image_size=768,
        patch_size=32,
        hidden_act="quick_gelu",
        layer_norm_eps=0.00001,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.image_size = image_size
        self.patch_size = patch_size
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
    @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)
        # get the vision config dict if we are loading from OwlViTConfig
        if config_dict.get("model_type") == "owlvit":
            config_dict = config_dict["vision_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 OwlViTConfig(PretrainedConfig):
    r"""
    [`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
    instantiate an OWL-ViT model according to the specified arguments, defining the text model and vision model
    configs.
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.
    Args:
        text_config_dict (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`OwlViTTextConfig`].
        vision_config_dict (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`OwlViTVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision projection layers.
        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
            The inital value of the *logit_scale* parameter. Default is used as per the original OWL-ViT
            implementation.
        kwargs (*optional*):
            Dictionary of keyword arguments.
    """
    model_type = "owlvit"
    is_composition = True
    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=512,
        logit_scale_init_value=2.6592,
        return_dict=True,
        **kwargs
    ):
        super().__init__(text_config=text_config, vision_config=vision_config, **kwargs)
        if text_config is None:
            text_config = {}
            logger.info("text_config_dict is None. Initializing the OwlViTTextConfig with default values.")
        if vision_config is None:
            vision_config = {}
            logger.info("vision_config_dict is None. initializing the OwlViTVisionConfig with default values.")
        self.text_config = OwlViTTextConfig(**text_config)
        self.vision_config = OwlViTVisionConfig(**vision_config)
        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.return_dict = return_dict
        self.initializer_factor = 1.0
    @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 "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)
    @classmethod
    def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
        r"""
        Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision
        model configuration.
        Returns:
            [`OwlViTConfig`]: An instance of a configuration object
        """
        config_dict = {}
        config_dict["text_config"] = text_config
        config_dict["vision_config"] = vision_config
        return cls.from_dict(config_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
--- a/src/transformers/models/owlvit/convert_owlvit_original_flax_to_hf.py
+++ b/src/transformers/models/owlvit/convert_owlvit_original_flax_to_hf.py
@@ -0,0 +1,407 @@
 # coding=utf-8
 # Copyright 2022 The 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.
 """Convert OWL-ViT checkpoints from the original repository. URL:
 https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit"""
 import argparse
 import collections
 import torch
 import torch.nn as nn
 import jax
 import jax.numpy as jnp
 from clip.model import CLIP
 from flax.training import checkpoints
 from huggingface_hub import Repository
 from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTFeatureExtractor,
    OwlViTForObjectDetection,
    OwlViTModel,
    OwlViTProcessor,
 )
 CONFIGS = {
    "vit_b32": dict(
        embed_dim=512,
        image_resolution=768,
        context_length=16,
        vocab_size=49408,
        vision_layers=12,
        vision_width=768,
        vision_patch_size=32,
        transformer_width=512,
        transformer_heads=8,
        transformer_layers=12,
    ),
    "vit_b16": dict(
        embed_dim=512,
        image_resolution=768,
        context_length=16,
        vocab_size=49408,
        vision_layers=12,
        vision_width=768,
        vision_patch_size=16,
        transformer_width=512,
        transformer_heads=8,
        transformer_layers=12,
    ),
    "vit_l14": dict(
        embed_dim=768,
        image_resolution=840,
        context_length=16,
        vocab_size=49408,
        vision_layers=24,
        vision_width=1024,
        vision_patch_size=14,
        transformer_width=768,
        transformer_heads=12,
        transformer_layers=12,
    ),
 }
 def flatten_nested_dict(params, parent_key="", sep="/"):
    items = []
    for k, v in params.items():
        new_key = parent_key + sep + k if parent_key else k
        if isinstance(v, collections.MutableMapping):
            items.extend(flatten_nested_dict(v, new_key, sep=sep).items())
        else:
            items.append((new_key, v))
    return dict(items)
 def to_f32(params):
    return jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, params)
 def copy_attn_layer(hf_attn_layer, pt_attn_layer):
    q_proj, k_proj, v_proj = pt_attn_layer.in_proj_weight.chunk(3, dim=0)
    q_proj_bias, k_proj_bias, v_proj_bias = pt_attn_layer.in_proj_bias.chunk(3, dim=0)
    out_proj_weights = pt_attn_layer.out_proj.weight
    out_proj_bias = pt_attn_layer.out_proj.bias
    hf_attn_layer.q_proj.weight.data = q_proj
    hf_attn_layer.q_proj.bias.data = q_proj_bias
    hf_attn_layer.k_proj.weight.data = k_proj
    hf_attn_layer.k_proj.bias.data = k_proj_bias
    hf_attn_layer.v_proj.weight.data = v_proj
    hf_attn_layer.v_proj.bias.data = v_proj_bias
    hf_attn_layer.out_proj.weight = out_proj_weights
    hf_attn_layer.out_proj.bias = out_proj_bias
 def copy_mlp(hf_mlp, pt_mlp):
    copy_linear(hf_mlp.fc1, pt_mlp.c_fc)
    copy_linear(hf_mlp.fc2, pt_mlp.c_proj)
 def copy_linear(hf_linear, pt_linear):
    hf_linear.weight = pt_linear.weight
    hf_linear.bias = pt_linear.bias
 def copy_layer(hf_layer, pt_layer):
    # copy layer norms
    copy_linear(hf_layer.layer_norm1, pt_layer.ln_1)
    copy_linear(hf_layer.layer_norm2, pt_layer.ln_2)
    # copy MLP
    copy_mlp(hf_layer.mlp, pt_layer.mlp)
    # copy attn
    copy_attn_layer(hf_layer.self_attn, pt_layer.attn)
 def copy_layers(hf_layers, pt_layers):
    for hf_layer, pt_layer in zip(hf_layers, pt_layers):
        copy_layer(hf_layer, pt_layer)
 def copy_encoder(hf_encoder, pt_model):
    # copy  embeds
    hf_encoder.embeddings.token_embedding.weight = pt_model.token_embedding.weight
    hf_encoder.embeddings.position_embedding.weight.data = pt_model.positional_embedding
    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm, pt_model.ln_final)
    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers, pt_model.transformer.resblocks)
 def copy_text_model_and_projection(hf_model, pt_model):
    # copy projection
    hf_model.text_projection.weight.data = pt_model.text_projection.data.T
    # copy text encoder
    copy_encoder(hf_model.text_model, pt_model)
 def copy_vision_model_and_projection(hf_model, pt_model):
    # copy projection
    hf_model.visual_projection.weight.data = pt_model.visual.proj.data.T
    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layernorm, pt_model.visual.ln_pre)
    copy_linear(hf_model.vision_model.post_layernorm, pt_model.visual.ln_post)
    # copy embeds
    hf_model.vision_model.embeddings.patch_embedding.weight.data = pt_model.visual.conv1.weight.data
    hf_model.vision_model.embeddings.class_embedding = pt_model.visual.class_embedding
    hf_model.vision_model.embeddings.position_embedding.weight.data = pt_model.visual.positional_embedding.data
    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers, pt_model.visual.transformer.resblocks)
 def copy_class_merge_token(hf_model, flax_params):
    flax_class_token_params = flatten_nested_dict(flax_params["backbone"]["merged_class_token"])
    weight = torch.from_numpy(flax_class_token_params["scale"])
    bias = torch.from_numpy(flax_class_token_params["bias"])
    hf_model.layer_norm.weight = nn.Parameter(weight)
    hf_model.layer_norm.bias = nn.Parameter(bias)
 def copy_class_box_heads(hf_model, flax_params):
    pt_params = hf_model.state_dict()
    new_params = {}
    # Rename class prediction head flax params to pytorch HF
    flax_class_params = flatten_nested_dict(flax_params["class_head"])
    for flax_key, v in flax_class_params.items():
        torch_key = flax_key.replace("/", ".")
        torch_key = torch_key.replace(".kernel", ".weight")
        torch_key = torch_key.replace("Dense_0", "dense0")
        torch_key = "class_head." + torch_key
        if "weight" in torch_key and v.ndim == 2:
            v = v.T
        new_params[torch_key] = nn.Parameter(torch.from_numpy(v))
    # Rename box prediction box flax params to pytorch HF
    flax_box_params = flatten_nested_dict(flax_params["obj_box_head"])
    for flax_key, v in flax_box_params.items():
        torch_key = flax_key.replace("/", ".")
        torch_key = torch_key.replace(".kernel", ".weight")
        torch_key = torch_key.replace("_", "").lower()
        torch_key = "box_head." + torch_key
        if "weight" in torch_key and v.ndim == 2:
            v = v.T
        new_params[torch_key] = nn.Parameter(torch.from_numpy(v))
    # Copy flax params to PyTorch params
    for name, param in new_params.items():
        if name in pt_params.keys():
            pt_params[name].copy_(param)
 def copy_flax_attn_params(hf_backbone, flax_attn_params):
    for k, v in flax_attn_params.items():
        if k.startswith("transformer"):
            torch_key = k.replace("transformer.resblocks", "text_model.encoder.layers")
        else:
            torch_key = k.replace("visual.transformer.resblocks", "vision_model.encoder.layers")
        torch_key = torch_key.replace("attn", "self_attn")
        torch_key = torch_key.replace("key", "k_proj")
        torch_key = torch_key.replace("value", "v_proj")
        torch_key = torch_key.replace("query", "q_proj")
        torch_key = torch_key.replace("out", "out_proj")
        if "bias" in torch_key and v.ndim == 2:
            shape = v.shape[0] * v.shape[1]
            v = v.reshape(shape)
        if "weight" in torch_key and "out" in torch_key:
            shape = (v.shape[0] * v.shape[1], v.shape[2])
            v = v.reshape(shape).T
        if "weight" in torch_key and "out" not in torch_key:
            shape = (v.shape[0], v.shape[1] * v.shape[2])
            v = v.reshape(shape).T
        # Copy flax CLIP attn params to HF PyTorch params
        v = torch.from_numpy(v)
        hf_backbone.state_dict()[torch_key].copy_(v)
 def _convert_attn_layers(params):
    new_params = {}
    processed_attn_layers = []
    for k, v in params.items():
        if "attn." in k:
            base = k[: k.rindex("attn.") + 5]
            if base in processed_attn_layers:
                continue
            processed_attn_layers.append(base)
            dim = params[base + "out.weight"].shape[-1]
            new_params[base + "out_proj.weight"] = params[base + "out.weight"].reshape(dim, dim).T
            new_params[base + "out_proj.bias"] = params[base + "out.bias"]
        else:
            new_params[k] = v
    return new_params
 def convert_clip_backbone(flax_params, torch_config):
    torch_model = CLIP(**torch_config)
    torch_model.eval()
    torch_clip_params = torch_model.state_dict()
    flax_clip_params = flatten_nested_dict(flax_params["backbone"]["clip"])
    new_torch_params = {}
    for flax_key, v in flax_clip_params.items():
        torch_key = flax_key.replace("/", ".")
        torch_key = torch_key.replace("text.token_embedding.embedding", "token_embedding.kernel")
        if (
            torch_key.startswith("text.transformer")
            or torch_key.startswith("text.text_projection")
            or torch_key.startswith("text.ln_final")
            or torch_key.startswith("text.positional_embedding")
        ):
            torch_key = torch_key[5:]
        torch_key = torch_key.replace("text_projection.kernel", "text_projection")
        torch_key = torch_key.replace("visual.proj.kernel", "visual.proj")
        torch_key = torch_key.replace(".scale", ".weight")
        torch_key = torch_key.replace(".kernel", ".weight")
        if "conv" in torch_key or "downsample.0.weight" in torch_key:
            v = v.transpose(3, 2, 0, 1)
        elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
            # Fully connected layers are transposed, embeddings are not
            v = v.T
        new_torch_params[torch_key] = v
    attn_params = _convert_attn_layers(new_torch_params)
    new_torch_params.update(attn_params)
    attn_params = {}
    # Copy flax CLIP backbone params to PyTorch params
    for name, param in new_torch_params.items():
        if name in torch_clip_params.keys():
            new_param = torch.from_numpy(new_torch_params[name])
            torch_clip_params[name].copy_(new_param)
        else:
            attn_params[name] = param
    return torch_clip_params, torch_model, attn_params
@torch.no_grad()
 def convert_owlvit_checkpoint(pt_backbone, flax_params, attn_params, pytorch_dump_folder_path, config_path=None):
    """
    Copy/paste/tweak model's weights to transformers design.
    """
    repo = Repository(pytorch_dump_folder_path, clone_from=f"google/{pytorch_dump_folder_path}")
    repo.git_pull()
    if config_path is not None:
        config = OwlViTConfig.from_pretrained(config_path)
    else:
        config = OwlViTConfig()
    hf_backbone = OwlViTModel(config).eval()
    hf_model = OwlViTForObjectDetection(config).eval()
    copy_text_model_and_projection(hf_backbone, pt_backbone)
    copy_vision_model_and_projection(hf_backbone, pt_backbone)
    hf_backbone.logit_scale = pt_backbone.logit_scale
    copy_flax_attn_params(hf_backbone, attn_params)
    hf_model.owlvit = hf_backbone
    copy_class_merge_token(hf_model, flax_params)
    copy_class_box_heads(hf_model, flax_params)
    # Save HF model
    hf_model.save_pretrained(repo.local_dir)
    # Initialize feature extractor
    feature_extractor = OwlViTFeatureExtractor(
        size=config.vision_config.image_size, crop_size=config.vision_config.image_size
    )
    # Initialize tokenizer
    tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32", pad_token="!", model_max_length=16)
    # Initialize processor
    processor = OwlViTProcessor(feature_extractor=feature_extractor, tokenizer=tokenizer)
    feature_extractor.save_pretrained(repo.local_dir)
    processor.save_pretrained(repo.local_dir)
    repo.git_add()
    repo.git_commit("Upload model and processor")
    repo.git_push()
 if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--owlvit_version",
        default=None,
        type=str,
        required=True,
        help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
    )
    parser.add_argument(
        "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
    )
    parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
    parser.add_argument(
        "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    # Initialize PyToch clip model
    model_name = args.owlvit_version
    if model_name == "clip_b16":
        torch_config = CONFIGS["vit_b16"]
    elif model_name == "clip_b32":
        torch_config = CONFIGS["vit_b32"]
    elif model_name == "clip_l14":
        torch_config = CONFIGS["vit_l14"]
    # Load from checkpoint and convert params to float-32
    variables = checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
    flax_params = jax.tree_map(lambda x: x.astype(jnp.float32) if x.dtype == jnp.bfloat16 else x, variables)
    del variables
    # Convert CLIP backbone
    pt_backbone_params, clip_pt, attn_params = convert_clip_backbone(flax_params, torch_config)
    convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)
--- a/src/transformers/models/owlvit/feature_extraction_owlvit.py
+++ b/src/transformers/models/owlvit/feature_extraction_owlvit.py
@@ -0,0 +1,210 @@
 # coding=utf-8
 # Copyright 2022 The 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.
 """Feature extractor class for OwlViT."""
 from typing import List, Optional, Union
 import numpy as np
 from PIL import Image
 from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
 from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
 from ...utils import TensorType, is_torch_available, logging
 if is_torch_available():
    import torch
    from torch import nn
 logger = logging.get_logger(__name__)
 def center_to_corners_format(x):
    """
    Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
    (left, top, right, bottom).
    """
    x_center, y_center, width, height = x.unbind(-1)
    boxes = [(x_center - 0.5 * width), (y_center - 0.5 * height), (x_center + 0.5 * width), (y_center + 0.5 * height)]
    return torch.stack(boxes, dim=-1)
 class OwlViTFeatureExtractor(FeatureExtractionMixin, ImageFeatureExtractionMixin):
    r"""
    Constructs an OWL-ViT feature extractor.
    This feature extractor inherits from [`FeatureExtractionMixin`] which contains most of the main methods. Users
    should refer to this superclass for more information regarding those methods.
    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the shorter edge of the input to a certain `size`.
        size (`int`, *optional*, defaults to 768):
            Resize the shorter edge of the input to the given size. Only has an effect if `do_resize` is set to `True`.
        resample (`int`, *optional*, defaults to `PIL.Image.BICUBIC`):
            An optional resampling filter. This can be one of `PIL.Image.NEAREST`, `PIL.Image.BOX`,
            `PIL.Image.BILINEAR`, `PIL.Image.HAMMING`, `PIL.Image.BICUBIC` or `PIL.Image.LANCZOS`. Only has an effect
            if `do_resize` is set to `True`.
        do_center_crop (`bool`, *optional*, defaults to `True`):
            Whether to crop the input at the center. If the input size is smaller than `crop_size` along any edge, the
            image is padded with 0's and then center cropped.
        crop_size (`int`, *optional*, defaults to 768):
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether or not to normalize the input with `image_mean` and `image_std`. Desired output size when applying
            center-cropping. Only has an effect if `do_center_crop` is set to `True`.
        image_mean (`List[int]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
            The sequence of means for each channel, to be used when normalizing images.
        image_std (`List[int]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
            The sequence of standard deviations for each channel, to be used when normalizing images.
    """
    model_input_names = ["pixel_values"]
    def __init__(
        self,
        do_resize=True,
        size=768,
        resample=Image.BICUBIC,
        crop_size=768,
        do_center_crop=True,
        do_normalize=True,
        image_mean=None,
        image_std=None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.size = size
        self.resample = resample
        self.crop_size = crop_size
        self.do_resize = do_resize
        self.do_center_crop = do_center_crop
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else [0.48145466, 0.4578275, 0.40821073]
        self.image_std = image_std if image_std is not None else [0.26862954, 0.26130258, 0.27577711]
    def post_process(self, outputs, target_sizes):
        """
        Converts the output of [`OwlViTForObjectDetection`] into the format expected by the COCO api.
        Args:
            outputs ([`OwlViTObjectDetectionOutput`]):
                Raw outputs of the model.
            target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
                Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original
                image size (before any data augmentation). For visualization, this should be the image size after data
                augment, but before padding.
        Returns:
            `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image
            in the batch as predicted by the model.
        """
        out_logits, out_bbox = outputs.logits, outputs.pred_boxes
        if len(out_logits) != len(target_sizes):
            raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits")
        if target_sizes.shape[1] != 2:
            raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch")
        prob = nn.functional.softmax(out_logits, -1)
        scores, labels = prob[..., :-1].max(-1)
        # Convert to [x0, y0, x1, y1] format
        boxes = center_to_corners_format(out_bbox)
        # Convert from relative [0, 1] to absolute [0, height] coordinates
        img_h, img_w = target_sizes.unbind(1)
        scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1)
        boxes = boxes * scale_fct[:, None, :]
        results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)]
        return results
    def __call__(
        self,
        images: Union[
            Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"]  # noqa
        ],
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several image(s).
        <Tip warning={true}>
        NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
        PIL images.
        </Tip>
        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W) or (H, W, C),
                where C is a number of channels, H and W are image height and width.
            return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'np'`):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.
        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
            - **pixel_values** -- Pixel values to be fed to a model.
        """
        # Input type checking for clearer error
        valid_images = False
        # Check that images has a valid type
        if isinstance(images, (Image.Image, np.ndarray)) or is_torch_tensor(images):
            valid_images = True
        elif isinstance(images, (list, tuple)):
            if isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]):
                valid_images = True
        if not valid_images:
            raise ValueError(
                "Images must of type `PIL.Image.Image`, `np.ndarray` or `torch.Tensor` (single example), "
                "`List[PIL.Image.Image]`, `List[np.ndarray]` or `List[torch.Tensor]` (batch of examples)."
            )
        is_batched = bool(
            isinstance(images, (list, tuple))
            and (isinstance(images[0], (Image.Image, np.ndarray)) or is_torch_tensor(images[0]))
        )
        if not is_batched:
            images = [images]
        # transformations (resizing + center cropping + normalization)
        if self.do_resize and self.size is not None and self.resample is not None:
            images = [
                self.resize(image=image, size=self.size, resample=self.resample, default_to_square=False)
                for image in images
            ]
        if self.do_center_crop and self.crop_size is not None:
            images = [self.center_crop(image, self.crop_size) for image in images]
        if self.do_normalize:
            images = [self.normalize(image=image, mean=self.image_mean, std=self.image_std) for image in images]
        # return as BatchFeature
        data = {"pixel_values": images}
        encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors)
        return encoded_inputs
--- a/src/transformers/models/owlvit/modeling_owlvit.py
+++ b/src/transformers/models/owlvit/modeling_owlvit.py
--- a/src/transformers/models/owlvit/processing_owlvit.py
+++ b/src/transformers/models/owlvit/processing_owlvit.py
@@ -0,0 +1,154 @@
 # coding=utf-8
 # Copyright 2022 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.
 """
 Image/Text processor class for OWL-ViT
 """
 from typing import List
 import numpy as np
 from transformers import is_flax_available, is_tf_available, is_torch_available
 from ...processing_utils import ProcessorMixin
 from ...tokenization_utils_base import BatchEncoding
 class OwlViTProcessor(ProcessorMixin):
    r"""
    Constructs an OWL-ViT processor which wraps [`OwlViTFeatureExtractor`] and [`CLIPTokenizer`]/[`CLIPTokenizerFast`]
    into a single processor that interits both the feature extractor and tokenizer functionalities. See the
    [`~OwlViTProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information.
    Args:
        feature_extractor ([`OwlViTFeatureExtractor`]):
            The feature extractor is a required input.
        tokenizer ([`CLIPTokenizer`, `CLIPTokenizerFast`]):
            The tokenizer is a required input.
    """
    feature_extractor_class = "OwlViTFeatureExtractor"
    tokenizer_class = ("CLIPTokenizer", "CLIPTokenizerFast")
    def __init__(self, feature_extractor, tokenizer):
        super().__init__(feature_extractor, tokenizer)
    def __call__(self, text=None, images=None, padding="max_length", return_tensors="np", **kwargs):
        """
        Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
        `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode:
        the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to
        CLIPFeatureExtractor's [`~CLIPFeatureExtractor.__call__`] if `images` is not `None`. Please refer to the
        doctsring of the above two methods for more information.
        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`,
            `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a
                number of channels, H and W are image height and width.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.
        Returns:
            [`BatchEncoding`]: A [`BatchEncoding`] with the following fields:
            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
        """
        if text is None and images is None:
            raise ValueError("You have to specify at least one text or image. Both cannot be none.")
        if text is not None:
            if isinstance(text, str) or (isinstance(text, List) and not isinstance(text[0], List)):
                encodings = [self.tokenizer(text, padding=padding, return_tensors=return_tensors, **kwargs)]
            elif isinstance(text, List) and isinstance(text[0], List):
                encodings = []
                # Maximum number of queries across batch
                max_num_queries = max([len(t) for t in text])
                # Pad all batch samples to max number of text queries
                for t in text:
                    if len(t) != max_num_queries:
                        t = t + [" "] * (max_num_queries - len(t))
                    encoding = self.tokenizer(t, padding=padding, return_tensors=return_tensors, **kwargs)
                    encodings.append(encoding)
            else:
                raise TypeError("Input text should be a string, a list of strings or a nested list of strings")
            if return_tensors == "np":
                input_ids = np.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
                attention_mask = np.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
            elif return_tensors == "jax" and is_flax_available():
                import jax.numpy as jnp
                input_ids = jnp.concatenate([encoding["input_ids"] for encoding in encodings], axis=0)
                attention_mask = jnp.concatenate([encoding["attention_mask"] for encoding in encodings], axis=0)
            elif return_tensors == "pt" and is_torch_available():
                import torch
                input_ids = torch.cat([encoding["input_ids"] for encoding in encodings], dim=0)
                attention_mask = torch.cat([encoding["attention_mask"] for encoding in encodings], dim=0)
            elif return_tensors == "tf" and is_tf_available():
                import tensorflow as tf
                input_ids = tf.stack([encoding["input_ids"] for encoding in encodings], axis=0)
                attention_mask = tf.stack([encoding["attention_mask"] for encoding in encodings], axis=0)
            else:
                raise ValueError("Target return tensor type could not be returned")
            encoding = BatchEncoding()
            encoding["input_ids"] = input_ids
            encoding["attention_mask"] = attention_mask
        if images is not None:
            image_features = self.feature_extractor(images, return_tensors=return_tensors, **kwargs)
        if text is not None and images is not None:
            encoding["pixel_values"] = image_features.pixel_values
            return encoding
        elif text is not None:
            return encoding
        else:
            return BatchEncoding(data=dict(**image_features), tensor_type=return_tensors)
    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)
    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)
--- a/src/transformers/utils/dummy_pt_objects.py
+++ b/src/transformers/utils/dummy_pt_objects.py
@@ -3459,6 +3459,44 @@ class OPTPreTrainedModel(metaclass=DummyObject):
        requires_backends(self, ["torch"])
 OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 class OwlViTForObjectDetection(metaclass=DummyObject):
    _backends = ["torch"]
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
 class OwlViTModel(metaclass=DummyObject):
    _backends = ["torch"]
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
 class OwlViTPreTrainedModel(metaclass=DummyObject):
    _backends = ["torch"]
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
 class OwlViTTextModel(metaclass=DummyObject):
    _backends = ["torch"]
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
 class OwlViTVisionModel(metaclass=DummyObject):
    _backends = ["torch"]
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["torch"])
 class PegasusForCausalLM(metaclass=DummyObject):
    _backends = ["torch"]
--- a/src/transformers/utils/dummy_vision_objects.py
+++ b/src/transformers/utils/dummy_vision_objects.py
@@ -122,6 +122,13 @@ class MobileViTFeatureExtractor(metaclass=DummyObject):
        requires_backends(self, ["vision"])
 class OwlViTFeatureExtractor(metaclass=DummyObject):
    _backends = ["vision"]
    def __init__(self, *args, **kwargs):
        requires_backends(self, ["vision"])
 class PerceiverFeatureExtractor(metaclass=DummyObject):
    _backends = ["vision"]
--- a/tests/models/owlvit/init.py
+++ b/tests/models/owlvit/init.py
--- a/tests/models/owlvit/test_feature_extraction_owlvit.py
+++ b/tests/models/owlvit/test_feature_extraction_owlvit.py
@@ -0,0 +1,201 @@
 # coding=utf-8
 # Copyright 2022 HuggingFace Inc.
 #
 # 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.
 import unittest
 import numpy as np
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
 if is_torch_available():
    import torch
 if is_vision_available():
    from PIL import Image
    from transformers import OwlViTFeatureExtractor
 class OwlViTFeatureExtractionTester(unittest.TestCase):
    def __init__(
        self,
        parent,
        batch_size=7,
        num_channels=3,
        image_size=18,
        min_resolution=30,
        max_resolution=400,
        do_resize=True,
        size=20,
        do_center_crop=True,
        crop_size=18,
        do_normalize=True,
        image_mean=[0.48145466, 0.4578275, 0.40821073],
        image_std=[0.26862954, 0.26130258, 0.27577711],
        do_convert_rgb=True,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_channels = num_channels
        self.image_size = image_size
        self.min_resolution = min_resolution
        self.max_resolution = max_resolution
        self.do_resize = do_resize
        self.size = size
        self.do_center_crop = do_center_crop
        self.crop_size = crop_size
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_convert_rgb = do_convert_rgb
    def prepare_feat_extract_dict(self):
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_convert_rgb": self.do_convert_rgb,
        }
@require_torch
@require_vision
 class OwlViTFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
    feature_extraction_class = OwlViTFeatureExtractor if is_vision_available() else None
    def setUp(self):
        self.feature_extract_tester = OwlViTFeatureExtractionTester(self)
    @property
    def feat_extract_dict(self):
        return self.feature_extract_tester.prepare_feat_extract_dict()
    def test_feat_extract_properties(self):
        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
        self.assertTrue(hasattr(feature_extractor, "do_resize"))
        self.assertTrue(hasattr(feature_extractor, "size"))
        self.assertTrue(hasattr(feature_extractor, "do_center_crop"))
        self.assertTrue(hasattr(feature_extractor, "center_crop"))
        self.assertTrue(hasattr(feature_extractor, "do_normalize"))
        self.assertTrue(hasattr(feature_extractor, "image_mean"))
        self.assertTrue(hasattr(feature_extractor, "image_std"))
        self.assertTrue(hasattr(feature_extractor, "do_convert_rgb"))
    def test_call_pil(self):
        # Initialize feature_extractor
        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
        # create random PIL images
        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
        for image in image_inputs:
            self.assertIsInstance(image, Image.Image)
        # Test not batched input
        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.feature_extract_tester.num_channels,
                self.feature_extract_tester.crop_size,
                self.feature_extract_tester.crop_size,
            ),
        )
        # Test batched
        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.feature_extract_tester.batch_size,
                self.feature_extract_tester.num_channels,
                self.feature_extract_tester.crop_size,
                self.feature_extract_tester.crop_size,
            ),
        )
    def test_call_numpy(self):
        # Initialize feature_extractor
        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
        # create random numpy tensors
        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, numpify=True)
        for image in image_inputs:
            self.assertIsInstance(image, np.ndarray)
        # Test not batched input
        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.feature_extract_tester.num_channels,
                self.feature_extract_tester.crop_size,
                self.feature_extract_tester.crop_size,
            ),
        )
        # Test batched
        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.feature_extract_tester.batch_size,
                self.feature_extract_tester.num_channels,
                self.feature_extract_tester.crop_size,
                self.feature_extract_tester.crop_size,
            ),
        )
    def test_call_pytorch(self):
        # Initialize feature_extractor
        feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
        # create random PyTorch tensors
        image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
        for image in image_inputs:
            self.assertIsInstance(image, torch.Tensor)
        # Test not batched input
        encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                1,
                self.feature_extract_tester.num_channels,
                self.feature_extract_tester.crop_size,
                self.feature_extract_tester.crop_size,
            ),
        )
        # Test batched
        encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
        self.assertEqual(
            encoded_images.shape,
            (
                self.feature_extract_tester.batch_size,
                self.feature_extract_tester.num_channels,
                self.feature_extract_tester.crop_size,
                self.feature_extract_tester.crop_size,
            ),
        )
--- a/tests/models/owlvit/test_modeling_owlvit.py
+++ b/tests/models/owlvit/test_modeling_owlvit.py
@@ -0,0 +1,815 @@
 # coding=utf-8
 # Copyright 2022 The 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.
 """ Testing suite for the PyTorch OwlViT model. """
 import inspect
 import os
 import tempfile
 import unittest
 from typing import Dict, List, Tuple
 import numpy as np
 import requests
 from transformers import OwlViTConfig, OwlViTTextConfig, OwlViTVisionConfig
 from transformers.testing_utils import require_torch, require_vision, slow, torch_device
 from transformers.utils import is_torch_available, is_vision_available
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import (
    ModelTesterMixin,
    _config_zero_init,
    floats_tensor,
    ids_tensor,
    random_attention_mask,
 )
 if is_torch_available():
    import torch
    from torch import nn
    from transformers import OwlViTForObjectDetection, OwlViTModel, OwlViTTextModel, OwlViTVisionModel
    from transformers.models.owlvit.modeling_owlvit import OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST
 if is_vision_available():
    from PIL import Image
    from transformers import OwlViTProcessor
 class OwlViTVisionModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        image_size=32,
        patch_size=2,
        num_channels=3,
        is_training=True,
        hidden_size=32,
        num_hidden_layers=5,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.scope = scope
        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        num_patches = (image_size // patch_size) ** 2
        self.seq_length = num_patches + 1
    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        config = self.get_config()
        return config, pixel_values
    def get_config(self):
        return OwlViTVisionConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            initializer_range=self.initializer_range,
        )
    def create_and_check_model(self, config, pixel_values):
        model = OwlViTVisionModel(config=config)
        model.to(torch_device)
        model.eval()
        pixel_values = pixel_values.to(torch.float32)
        with torch.no_grad():
            result = model(pixel_values)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        num_patches = (self.image_size // self.patch_size) ** 2
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, num_patches + 1, self.hidden_size))
    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict
@require_torch
 class OwlViTVisionModelTest(ModelTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as OWLVIT does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """
    all_model_classes = (OwlViTVisionModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    def setUp(self):
        self.model_tester = OwlViTVisionModelTester(self)
        self.config_tester = ConfigTester(
            self, config_class=OwlViTVisionConfig, has_text_modality=False, hidden_size=37
        )
    def test_config(self):
        self.config_tester.run_common_tests()
    @unittest.skip(reason="OWLVIT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass
    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))
    def test_forward_signature(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
            model = model_class(config)
            signature = inspect.signature(model.forward)
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            arg_names = [*signature.parameters.keys()]
            expected_arg_names = ["pixel_values"]
            self.assertListEqual(arg_names[:1], expected_arg_names)
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)
    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training(self):
        pass
    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training_gradient_checkpointing(self):
        pass
    @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass
    @unittest.skip(reason="OwlViTVisionModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass
    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTVisionModel.from_pretrained(model_name)
            self.assertIsNotNone(model)
 class OwlViTTextModelTester:
    def __init__(
        self,
        parent,
        batch_size=12,
        num_queries=4,
        seq_length=16,
        is_training=True,
        use_input_mask=True,
        use_labels=True,
        vocab_size=99,
        hidden_size=64,
        num_hidden_layers=12,
        num_attention_heads=4,
        intermediate_size=37,
        dropout=0.1,
        attention_dropout=0.1,
        max_position_embeddings=16,
        initializer_range=0.02,
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.num_queries = num_queries
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_labels = use_labels
        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.intermediate_size = intermediate_size
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.scope = scope
    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size * self.num_queries, self.seq_length], self.vocab_size)
        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size * self.num_queries, self.seq_length])
        if input_mask is not None:
            num_text, seq_length = input_mask.shape
            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(num_text,))
            for idx, start_index in enumerate(rnd_start_indices):
                input_mask[idx, :start_index] = 1
                input_mask[idx, start_index:] = 0
        config = self.get_config()
        return config, input_ids, input_mask
    def get_config(self):
        return OwlViTTextConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            dropout=self.dropout,
            attention_dropout=self.attention_dropout,
            max_position_embeddings=self.max_position_embeddings,
            initializer_range=self.initializer_range,
        )
    def create_and_check_model(self, config, input_ids, input_mask):
        model = OwlViTTextModel(config=config)
        model.to(torch_device)
        model.eval()
        with torch.no_grad():
            result = model(input_ids=input_ids, attention_mask=input_mask)
        self.parent.assertEqual(
            result.last_hidden_state.shape, (self.batch_size * self.num_queries, self.seq_length, self.hidden_size)
        )
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size * self.num_queries, self.hidden_size))
    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, input_mask = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict
@require_torch
 class OwlViTTextModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (OwlViTTextModel,) if is_torch_available() else ()
    fx_compatible = False
    test_pruning = False
    test_head_masking = False
    def setUp(self):
        self.model_tester = OwlViTTextModelTester(self)
        self.config_tester = ConfigTester(self, config_class=OwlViTTextConfig, hidden_size=37)
    def test_config(self):
        self.config_tester.run_common_tests()
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)
    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training(self):
        pass
    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training_gradient_checkpointing(self):
        pass
    @unittest.skip(reason="OWLVIT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass
    @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_from_base(self):
        pass
    @unittest.skip(reason="OwlViTTextModel has no base class and is not available in MODEL_MAPPING")
    def test_save_load_fast_init_to_base(self):
        pass
    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTTextModel.from_pretrained(model_name)
            self.assertIsNotNone(model)
 class OwlViTModelTester:
    def __init__(self, parent, is_training=True):
        self.parent = parent
        self.text_model_tester = OwlViTTextModelTester(parent)
        self.vision_model_tester = OwlViTVisionModelTester(parent)
        self.is_training = is_training
        self.text_config = self.text_model_tester.get_config().to_dict()
        self.vision_config = self.vision_model_tester.get_config().to_dict()
    def prepare_config_and_inputs(self):
        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        config = self.get_config()
        return config, input_ids, attention_mask, pixel_values
    def get_config(self):
        return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)
    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
        model = OwlViTModel(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(
                input_ids=input_ids,
                pixel_values=pixel_values,
                attention_mask=attention_mask,
            )
        image_logits_size = (
            self.vision_model_tester.batch_size,
            self.text_model_tester.batch_size * self.text_model_tester.num_queries,
        )
        text_logits_size = (
            self.text_model_tester.batch_size * self.text_model_tester.num_queries,
            self.vision_model_tester.batch_size,
        )
        self.parent.assertEqual(result.logits_per_image.shape, image_logits_size)
        self.parent.assertEqual(result.logits_per_text.shape, text_logits_size)
    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, input_ids, attention_mask, pixel_values = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
            "return_loss": False,
        }
        return config, inputs_dict
@require_torch
 class OwlViTModelTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (OwlViTModel,) if is_torch_available() else ()
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    def setUp(self):
        self.model_tester = OwlViTModelTester(self)
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)
    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass
    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass
    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass
    @unittest.skip(reason="OwlViTModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass
    # override as the `logit_scale` parameter initilization is different for OWLVIT
    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            for name, param in model.named_parameters():
                if param.requires_grad:
                    # check if `logit_scale` is initilized as per the original implementation
                    if name == "logit_scale":
                        self.assertAlmostEqual(
                            param.data.item(),
                            np.log(1 / 0.07),
                            delta=1e-3,
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
                    else:
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item(),
                            [0.0, 1.0],
                            msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                        )
    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            return
        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            try:
                input_ids = inputs_dict["input_ids"]
                pixel_values = inputs_dict["pixel_values"]  # OWLVIT needs pixel_values
                traced_model = torch.jit.trace(model, (input_ids, pixel_values))
            except RuntimeError:
                self.fail("Couldn't trace module.")
            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")
                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")
            model.to(torch_device)
            model.eval()
            loaded_model.to(torch_device)
            loaded_model.eval()
            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()
            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False
            self.assertTrue(models_equal)
    def test_load_vision_text_config(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        # Save OwlViTConfig and check if we can load OwlViTVisionConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            vision_config = OwlViTVisionConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
        # Save OwlViTConfig and check if we can load OwlViTTextConfig from it
        with tempfile.TemporaryDirectory() as tmp_dir_name:
            config.save_pretrained(tmp_dir_name)
            text_config = OwlViTTextConfig.from_pretrained(tmp_dir_name)
            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTModel.from_pretrained(model_name)
            self.assertIsNotNone(model)
 class OwlViTForObjectDetectionTester:
    def __init__(self, parent, is_training=True):
        self.parent = parent
        self.text_model_tester = OwlViTTextModelTester(parent)
        self.vision_model_tester = OwlViTVisionModelTester(parent)
        self.is_training = is_training
        self.text_config = self.text_model_tester.get_config().to_dict()
        self.vision_config = self.vision_model_tester.get_config().to_dict()
    def prepare_config_and_inputs(self):
        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
        config = self.get_config()
        return config, pixel_values, input_ids, attention_mask
    def get_config(self):
        return OwlViTConfig.from_text_vision_configs(self.text_config, self.vision_config, projection_dim=64)
    def create_and_check_model(self, config, pixel_values, input_ids, attention_mask):
        model = OwlViTForObjectDetection(config).to(torch_device).eval()
        with torch.no_grad():
            result = model(
                pixel_values=pixel_values,
                input_ids=input_ids,
                attention_mask=attention_mask,
                return_dict=True,
            )
        pred_boxes_size = (
            self.vision_model_tester.batch_size,
            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
            4,
        )
        pred_logits_size = (
            self.vision_model_tester.batch_size,
            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
            4,
        )
        pred_class_embeds_size = (
            self.vision_model_tester.batch_size,
            (self.vision_model_tester.image_size // self.vision_model_tester.patch_size) ** 2,
            self.text_model_tester.hidden_size,
        )
        self.parent.assertEqual(result.pred_boxes.shape, pred_boxes_size)
        self.parent.assertEqual(result.logits.shape, pred_logits_size)
        self.parent.assertEqual(result.class_embeds.shape, pred_class_embeds_size)
    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, input_ids, attention_mask = config_and_inputs
        inputs_dict = {
            "pixel_values": pixel_values,
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
        return config, inputs_dict
@require_torch
 class OwlViTForObjectDetectionTest(ModelTesterMixin, unittest.TestCase):
    all_model_classes = (OwlViTForObjectDetection,) if is_torch_available() else ()
    fx_compatible = False
    test_head_masking = False
    test_pruning = False
    test_resize_embeddings = False
    test_attention_outputs = False
    def setUp(self):
        self.model_tester = OwlViTForObjectDetectionTester(self)
    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)
    @unittest.skip(reason="Hidden_states is tested in individual model tests")
    def test_hidden_states_output(self):
        pass
    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
    def test_inputs_embeds(self):
        pass
    @unittest.skip(reason="Retain_grad is tested in individual model tests")
    def test_retain_grad_hidden_states_attentions(self):
        pass
    @unittest.skip(reason="OwlViTModel does not have input/output embeddings")
    def test_model_common_attributes(self):
        pass
    @unittest.skip(reason="Test_initialization is tested in individual model tests")
    def test_initialization(self):
        pass
    @unittest.skip(reason="Test_forward_signature is tested in individual model tests")
    def test_forward_signature(self):
        pass
    @unittest.skip(reason="Test_save_load_fast_init_from_base is tested in individual model tests")
    def test_save_load_fast_init_from_base(self):
        pass
    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training(self):
        pass
    @unittest.skip(reason="OWL-ViT does not support training yet")
    def test_training_gradient_checkpointing(self):
        pass
    def _create_and_check_torchscript(self, config, inputs_dict):
        if not self.test_torchscript:
            return
        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
        configs_no_init.torchscript = True
        configs_no_init.return_dict = False
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            model.to(torch_device)
            model.eval()
            try:
                input_ids = inputs_dict["input_ids"]
                pixel_values = inputs_dict["pixel_values"]  # OWLVIT needs pixel_values
                traced_model = torch.jit.trace(model, (input_ids, pixel_values))
            except RuntimeError:
                self.fail("Couldn't trace module.")
            with tempfile.TemporaryDirectory() as tmp_dir_name:
                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
                try:
                    torch.jit.save(traced_model, pt_file_name)
                except Exception:
                    self.fail("Couldn't save module.")
                try:
                    loaded_model = torch.jit.load(pt_file_name)
                except Exception:
                    self.fail("Couldn't load module.")
            model.to(torch_device)
            model.eval()
            loaded_model.to(torch_device)
            loaded_model.eval()
            model_state_dict = model.state_dict()
            loaded_model_state_dict = loaded_model.state_dict()
            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
            models_equal = True
            for layer_name, p1 in model_state_dict.items():
                p2 = loaded_model_state_dict[layer_name]
                if p1.data.ne(p2.data).sum() > 0:
                    models_equal = False
            self.assertTrue(models_equal)
    def test_model_outputs_equivalence(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
        def set_nan_tensor_to_zero(t):
            t[t != t] = 0
            return t
        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
            with torch.no_grad():
                tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
                dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
                def recursive_check(tuple_object, dict_object):
                    if isinstance(tuple_object, (List, Tuple)):
                        for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif isinstance(tuple_object, Dict):
                        for tuple_iterable_value, dict_iterable_value in zip(
                            tuple_object.values(), dict_object.values()
                        ):
                            recursive_check(tuple_iterable_value, dict_iterable_value)
                    elif tuple_object is None:
                        return
                    else:
                        self.assertTrue(
                            torch.allclose(
                                set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5
                            ),
                            msg=(
                                "Tuple and dict output are not equal. Difference:"
                                f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:"
                                f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has"
                                f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}."
                            ),
                        )
                recursive_check(tuple_output, dict_output)
        for model_class in self.all_model_classes:
            model = model_class(config)
            model.to(torch_device)
            model.eval()
            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
            check_equivalence(model, tuple_inputs, dict_inputs)
    @slow
    def test_model_from_pretrained(self):
        for model_name in OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            model = OwlViTForObjectDetection.from_pretrained(model_name)
            self.assertIsNotNone(model)
 # We will verify our results on an image of cute cats
 def prepare_img():
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    im = Image.open(requests.get(url, stream=True).raw)
    return im
@require_vision
@require_torch
 class OwlViTModelIntegrationTest(unittest.TestCase):
    @slow
    def test_inference(self):
        model_name = "google/owlvit-base-patch32"
        model = OwlViTModel.from_pretrained(model_name).to(torch_device)
        processor = OwlViTProcessor.from_pretrained(model_name)
        image = prepare_img()
        inputs = processor(
            text=[["a photo of a cat", "a photo of a dog"]],
            images=image,
            max_length=16,
            padding="max_length",
            return_tensors="pt",
        ).to(torch_device)
        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)
        # verify the logits
        self.assertEqual(
            outputs.logits_per_image.shape,
            torch.Size(
                (
                    inputs.pixel_values.shape[0],
                    inputs.input_ids.shape[0] * inputs.input_ids.shape[1] * inputs.pixel_values.shape[0],
                )
            ),
        )
        self.assertEqual(
            outputs.logits_per_text.shape,
            torch.Size(
                (
                    inputs.input_ids.shape[0] * inputs.input_ids.shape[1] * inputs.pixel_values.shape[0],
                    inputs.pixel_values.shape[0],
                )
            ),
        )
        expected_logits = torch.tensor([[1.0115, 0.9982]], device=torch_device)
        self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
    @slow
    def test_inference_object_detection(self):
        model_name = "google/owlvit-base-patch32"
        model = OwlViTForObjectDetection.from_pretrained(model_name).to(torch_device)
        processor = OwlViTProcessor.from_pretrained(model_name)
        image = prepare_img()
        inputs = processor(
            text=[["a photo of a cat", "a photo of a dog"]],
            images=image,
            max_length=16,
            padding="max_length",
            return_tensors="pt",
        ).to(torch_device)
        with torch.no_grad():
            outputs = model(**inputs)
        num_queries = int((model.config.vision_config.image_size / model.config.vision_config.patch_size) ** 2)
        self.assertEqual(outputs.pred_boxes.shape, torch.Size((1, num_queries, 4)))
        expected_slice_boxes = torch.tensor(
            [[0.0143, 0.0236, 0.0285], [0.0649, 0.0247, 0.0437], [0.0601, 0.0446, 0.0699]]
        ).to(torch_device)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, atol=1e-4))
--- a/tests/models/owlvit/test_processor_owlvit.py
+++ b/tests/models/owlvit/test_processor_owlvit.py
@@ -0,0 +1,241 @@
 # Copyright 2022 The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with 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.
 import json
 import os
 import shutil
 import tempfile
 import unittest
 import numpy as np
 import pytest
 from transformers import CLIPTokenizer, CLIPTokenizerFast
 from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
 from transformers.testing_utils import require_vision
 from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
 if is_vision_available():
    from PIL import Image
    from transformers import OwlViTFeatureExtractor, OwlViTProcessor
@require_vision
 class OwlViTProcessorTest(unittest.TestCase):
    def setUp(self):
        self.tmpdirname = tempfile.mkdtemp()
        # fmt: off
        vocab = ["", "l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
        # fmt: on
        vocab_tokens = dict(zip(vocab, range(len(vocab))))
        merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
        self.special_tokens_map = {"unk_token": "<unk>"}
        self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
        self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
        with open(self.vocab_file, "w", encoding="utf-8") as fp:
            fp.write(json.dumps(vocab_tokens) + "\n")
        with open(self.merges_file, "w", encoding="utf-8") as fp:
            fp.write("\n".join(merges))
        feature_extractor_map = {
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.48145466, 0.4578275, 0.40821073],
            "image_std": [0.26862954, 0.26130258, 0.27577711],
        }
        self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
        with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
            json.dump(feature_extractor_map, fp)
    def get_tokenizer(self, **kwargs):
        return CLIPTokenizer.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
    def get_rust_tokenizer(self, **kwargs):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
    def get_feature_extractor(self, **kwargs):
        return OwlViTFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
    def tearDown(self):
        shutil.rmtree(self.tmpdirname)
    def prepare_image_inputs(self):
        """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
        or a list of PyTorch tensors if one specifies torchify=True.
        """
        image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
        image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
        return image_inputs
    def test_save_load_pretrained_default(self):
        tokenizer_slow = self.get_tokenizer()
        tokenizer_fast = self.get_rust_tokenizer()
        feature_extractor = self.get_feature_extractor()
        processor_slow = OwlViTProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
        processor_slow.save_pretrained(self.tmpdirname)
        processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False)
        processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
        processor_fast.save_pretrained(self.tmpdirname)
        processor_fast = OwlViTProcessor.from_pretrained(self.tmpdirname)
        self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
        self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
        self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
        self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
        self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)
        self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string())
        self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string())
        self.assertIsInstance(processor_slow.feature_extractor, OwlViTFeatureExtractor)
        self.assertIsInstance(processor_fast.feature_extractor, OwlViTFeatureExtractor)
    def test_save_load_pretrained_additional_features(self):
        processor = OwlViTProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
        processor.save_pretrained(self.tmpdirname)
        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
        feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False)
        processor = OwlViTProcessor.from_pretrained(
            self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False
        )
        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
        self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.feature_extractor, OwlViTFeatureExtractor)
    def test_feature_extractor(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()
        processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
        image_input = self.prepare_image_inputs()
        input_feat_extract = feature_extractor(image_input, return_tensors="np")
        input_processor = processor(images=image_input, return_tensors="np")
        for key in input_feat_extract.keys():
            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
    def test_tokenizer(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()
        processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
        input_str = "lower newer"
        encoded_processor = processor(text=input_str, return_tensors="np")
        encoded_tok = tokenizer(input_str, return_tensors="np")
        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist())
    def test_processor(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()
        processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
        input_str = "lower newer"
        image_input = self.prepare_image_inputs()
        inputs = processor(text=input_str, images=image_input)
        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()
    def test_processor_with_text_list(self):
        model_name = "google/owlvit-base-patch32"
        processor = OwlViTProcessor.from_pretrained(model_name)
        input_text = ["cat", "nasa badge"]
        inputs = processor(text=input_text)
        seq_length = 16
        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
        self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()
    def test_processor_with_nested_text_list(self):
        model_name = "google/owlvit-base-patch32"
        processor = OwlViTProcessor.from_pretrained(model_name)
        input_texts = [["cat", "nasa badge"], ["person"]]
        inputs = processor(text=input_texts)
        seq_length = 16
        batch_size = len(input_texts)
        num_max_text_queries = max([len(texts) for texts in input_texts])
        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
        self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length))
        # test if it raises when no input is passed
        with pytest.raises(ValueError):
            processor()
    def test_processor_case(self):
        model_name = "google/owlvit-base-patch32"
        processor = OwlViTProcessor.from_pretrained(model_name)
        input_texts = ["cat", "nasa badge"]
        inputs = processor(text=input_texts)
        seq_length = 16
        input_ids = inputs["input_ids"]
        predicted_ids = [
            [49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ]
        self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
        self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
        self.assertListEqual(list(input_ids[0]), predicted_ids[0])
        self.assertListEqual(list(input_ids[1]), predicted_ids[1])
    def test_tokenizer_decode(self):
        feature_extractor = self.get_feature_extractor()
        tokenizer = self.get_tokenizer()
        processor = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
        decoded_processor = processor.batch_decode(predicted_ids)
        decoded_tok = tokenizer.batch_decode(predicted_ids)
        self.assertListEqual(decoded_tok, decoded_processor)
--- a/utils/check_config_docstrings.py
+++ b/utils/check_config_docstrings.py
@@ -41,6 +41,7 @@ _re_checkpoint = re.compile("\[(.+?)\]\((https://huggingface\.co/.+?)\)")
 CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK = {
    "CLIPConfig",
    "OwlViTConfig",
    "GroupViTConfig",
    "DecisionTransformerConfig",
    "EncoderDecoderConfig",
--- a/utils/check_repo.py
+++ b/utils/check_repo.py
@@ -166,6 +166,9 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
    "LukeForEntityPairClassification",
    "LukeForEntitySpanClassification",
    "OpenAIGPTDoubleHeadsModel",
    "OwlViTTextModel",
    "OwlViTVisionModel",
    "OwlViTForObjectDetection",
    "RagModel",
    "RagSequenceForGeneration",
    "RagTokenForGeneration",