# FLAVA

## FLAVA

### Overview

The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.

The paper aims at creating a single unified foundation model which can work across vision, language as well as vision-and-language multimodal tasks.

The abstract from the paper is the following:

*State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally, such models are often either cross-modal (contrastive) or multi-modal (with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising direction would be to use a single holistic universal model, as a “foundation”, that targets all modalities at once — a true vision and language foundation model should be good at vision tasks, language tasks, and cross- and multi-modal vision and language tasks. We introduce FLAVA as such a model and demonstrate impressive performance on a wide range of 35 tasks spanning these target modalities.*

This model was contributed by [aps](https://huggingface.co/aps). The original code can be found [here](https://github.com/facebookresearch/multimodal/tree/main/examples/flava).

### FlavaConfig

#### class transformers.FlavaConfig

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/configuration_flava.py#L467)

( image\_config: typing.Dict\[str, typing.Any] = Nonetext\_config: typing.Dict\[str, typing.Any] = Nonemultimodal\_config: typing.Dict\[str, typing.Any] = Noneimage\_codebook\_config: typing.Dict\[str, typing.Any] = Nonehidden\_size: int = 768layer\_norm\_eps: float = 1e-12projection\_dim: int = 768init\_codebook: bool = Truelogit\_scale\_init\_value: float = 2.6592initializer\_range: float = 0.02ce\_ignore\_index: int = -100mim\_weight: float = 1.0mlm\_weight: float = 1.0global\_contrastive\_weight: float = 1.0itm\_weight: float = 1.0mmm\_image\_weight: float = 1.0mmm\_text\_weight: float = 1.0global\_backprop\_contrastive: bool = Trueskip\_unmasked\_multimodal\_encoder: bool = Truereturn\_loss: bool = True\*\*kwargs )

Parameters

* **text\_config** (`dict`, *optional*) — Dictionary of configuration options used to initialize [FlavaTextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextConfig).
* **image\_config** (`dict`, *optional*) — Dictionary of configuration options used to initialize [FlavaImageConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageConfig).
* **multimodal\_config** (`dict`, *optional*) — Dictionary of configuration options used to initialize [FlavaMultimodalConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalConfig).
* **hidden\_size** (`int`, *optional*, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
* **layer\_norm\_eps** (`float`, *optional*, defaults to 1e-12) — The epsilon used by the layer normalization layers.
* **projection\_dim** (`int`, *optional*, defaults to 512) — Dimentionality of text and image projection layers.
* **logit\_scale\_init\_value** (`float`, *optional*, defaults to 2.6592) — The inital value of the *logit\_scale* paramter. Default is used as per the original FLAVA/CLIP implementation.
* **initializer\_range** (`float`, *optional*, defaults to 0.02) — The standard deviation of the truncated\_normal\_initializer for initializing all weight matrices.
* **ce\_ignore\_index** (`int`, *optional*, defaults to -100) — Cross entropy index to ignore.
* **mim\_weight** (`float`, *optional*, defaults to 1.0) — Weight to be assigned to MIM (Masked Image Modeling) unimodal loss
* **mlm\_weight** (`float`, *optional*, defaults to 1.0) — Weight to be assigned to MLM (Masked Language Modeling) unimodal loss
* **global\_contrastive\_weight** (`float`, *optional*, defaults to 1.0) — Weight to be assigned to global contrastive cross-alignment loss.
* **itm\_weight** (`float`, *optional*, defaults to 1.0) — Weight to be assigned to image-text matching multimodal loss.
* **mmm\_image\_weight** (`float`, *optional*, defaults to 1.0) — Weight to be assigned to MMM loss’s image part.
* **mmm\_text\_weight** (`float`, *optional*, defaults to 1.0) — Weight to be assigned to MMM loss’s text part.
* **global\_backprop\_contrastive** (`bool`, *optional*, defaults to `True`) — Whether to use global backpropgation through all workers in contrastive loss.
* **skip\_unmasked\_multimodal\_encoder** (`bool`, *optional*, defaults to `True`) — Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses.
* **return\_loss** (`bool`, *optional*, defaults to `True`) — Whether to return loss or not
* **kwargs** (*optional*) — Dictionary of keyword arguments.

[FlavaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaConfig) is the configuration class to store the configuration of a [FlavaModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaModel). It is used to instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.

Configuration objects inherit from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

Copied

```
>>> from transformers import FlavaConfig, FlavaModel, FlavaForPreTraining

>>> # Initializing a FlavaConfig with style configuration
>>> configuration = FlavaConfig()

>>> # Initializing a FlavaModel and FlavaForPreTraining model (with random weights) from the style configuration
>>> model = FlavaModel(configuration)
>>> model_pre = FlavaForPreTraining(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
>>> configuration_pre = model_pre.config
```

**from\_configs**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/configuration_flava.py#L741)

( image\_config: FlavaImageConfigtext\_config: FlavaTextConfigmultimodal\_config: FlavaMultimodalConfigimage\_codebook\_config: FlavaImageCodebookConfig\*\*kwargs ) → [FlavaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaConfig)

Returns

[FlavaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaConfig)

An instance of a configuration object

Instantiate a [FlavaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaConfig) (or a derived class) from flava text model configuration, flava image model configuration, flava multimodal model and flava codebook model configuration.

### FlavaTextConfig

#### class transformers.FlavaTextConfig

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/configuration_flava.py#L150)

( vocab\_size: int = 30522type\_vocab\_size: int = 2max\_position\_embeddings: int = 512position\_embedding\_type: str = 'absolute'hidden\_size: int = 768num\_hidden\_layers: int = 12num\_attention\_heads: int = 12intermediate\_size: int = 3072hidden\_act: str = 'gelu'hidden\_dropout\_prob: float = 0.0attention\_probs\_dropout\_prob: float = 0.0initializer\_range: float = 0.02layer\_norm\_eps: float = 1e-12pad\_token\_id: int = 0qkv\_bias: bool = True\*\*kwargs )

Parameters

* **vocab\_size** (`int`, *optional*, defaults to 30522) — Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **type\_vocab\_size** (`int`, *optional*, defaults to 2) — The vocabulary size of the `token_type_ids` passed when calling [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel). Note that even though text encoder allows `token_type_ids`’s value as 2, for text-only pretraining and fine-tuning, only 1 is used similar to RoBERTa.
* **max\_position\_embeddings** (`int`, *optional*, defaults to 512) — 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). For VL, max\_length passed to model is 77.
* **position\_embedding\_type** (`str`, *optional*, defaults to `"absolute"`) — Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
* **hidden\_size** (`int`, *optional*, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
* **num\_hidden\_layers** (`int`, *optional*, defaults to 12) — Number of hidden layers in the Transformer encoder.
* **num\_attention\_heads** (`int`, *optional*, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.
* **intermediate\_size** (`int`, *optional*, defaults to 3072) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
* **hidden\_act** (`str` or `function`, *optional*, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
* **hidden\_dropout\_prob** (`float`, *optional*, defaults to 0.1) — The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
* **attention\_probs\_dropout\_prob** (`float`, *optional*, defaults to 0.1) — The dropout ratio for the attention probabilities.
* **initializer\_range** (`float`, *optional*, defaults to 0.02) — The standard deviation of the truncated\_normal\_initializer for initializing all weight matrices.
* **layer\_norm\_eps** (`float`, *optional*, defaults to 1e-12) — The epsilon used by the layer normalization layers.
* **image\_size** (`int`, *optional*, defaults to 224) — The size (resolution) of each image.
* **patch\_size** (`int`, *optional*, defaults to 16) — The size (resolution) of each patch.
* **num\_channels** (`int`, *optional*, defaults to 3) — The number of input channels.
* **qkv\_bias** (`bool`, *optional*, defaults to `True`) — Whether to add a bias to the queries, keys and values.

This is the configuration class to store the configuration of a [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel). It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture.

Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.

Configuration objects inherit from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

Copied

```
>>> from transformers import FlavaTextConfig, FlavaTextModel

>>> # Initializing a FlavaTextModel with  style configuration
>>> configuration = FlavaTextConfig()

>>> # Initializing a FlavaTextModel model (with random weights) from the style configuration
>>> model = FlavaTextModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

### FlavaImageConfig

#### class transformers.FlavaImageConfig

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/configuration_flava.py#L31)

( hidden\_size: int = 768num\_hidden\_layers: int = 12num\_attention\_heads: int = 12intermediate\_size: int = 3072hidden\_act: int = 'gelu'hidden\_dropout\_prob: float = 0.0attention\_probs\_dropout\_prob: float = 0.0initializer\_range: float = 0.02layer\_norm\_eps: float = 1e-12image\_size: int = 224patch\_size: int = 16num\_channels: int = 3qkv\_bias: bool = Truemask\_token: bool = Truevocab\_size: int = 8192\*\*kwargs )

Parameters

* **hidden\_size** (`int`, *optional*, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
* **num\_hidden\_layers** (`int`, *optional*, defaults to 12) — Number of hidden layers in the Transformer encoder.
* **num\_attention\_heads** (`int`, *optional*, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.
* **intermediate\_size** (`int`, *optional*, defaults to 3072) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
* **hidden\_act** (`str` or `function`, *optional*, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
* **hidden\_dropout\_prob** (`float`, *optional*, defaults to 0.1) — The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
* **attention\_probs\_dropout\_prob** (`float`, *optional*, defaults to 0.1) — The dropout ratio for the attention probabilities.
* **initializer\_range** (`float`, *optional*, defaults to 0.02) — The standard deviation of the truncated\_normal\_initializer for initializing all weight matrices.
* **layer\_norm\_eps** (`float`, *optional*, defaults to 1e-12) — The epsilon used by the layer normalization layers.
* **image\_size** (`int`, *optional*, defaults to 224) — The size (resolution) of each image.
* **patch\_size** (`int`, *optional*, defaults to 16) — The size (resolution) of each patch.
* **num\_channels** (`int`, *optional*, defaults to 3) — The number of input channels.
* **qkv\_bias** (`bool`, *optional*, defaults to `True`) — Whether to add a bias to the queries, keys and values.
* **mask\_token** (`bool`, *optional*, defaults to `True`) — Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA.
* **vocab\_size** (`int`, *optional*, defaults to 8192) — Vocabulary size of the [FlavaImageCodebook](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageCodebook) used in conjunction with [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel) for MIM (Masked Image Modeling) loss for FLAVA.

This is the configuration class to store the configuration of a [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel). It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture.

Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.

Configuration objects inherit from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

Copied

```
>>> from transformers import FlavaImageConfig, FlavaImageModel

>>> # Initializing a FlavaImageModel with  style configuration
>>> configuration = FlavaImageConfig()

>>> # Initializing a FlavaImageModel model (with random weights) from the style configuration
>>> model = FlavaImageModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

### FlavaMultimodalConfig

#### class transformers.FlavaMultimodalConfig

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/configuration_flava.py#L279)

( hidden\_size: int = 768num\_hidden\_layers: int = 6num\_attention\_heads: int = 12intermediate\_size: int = 3072hidden\_act: int = 'gelu'hidden\_dropout\_prob: int = 0.0attention\_probs\_dropout\_prob: int = 0.0initializer\_range: float = 0.02layer\_norm\_eps: float = 1e-12qkv\_bias: bool = Trueuse\_cls\_token: bool = True\*\*kwargs )

Parameters

* **hidden\_size** (`int`, *optional*, defaults to 768) — Dimensionality of the encoder layers and the pooler layer.
* **num\_hidden\_layers** (`int`, *optional*, defaults to 12) — Number of hidden layers in the Transformer encoder.
* **num\_attention\_heads** (`int`, *optional*, defaults to 12) — Number of attention heads for each attention layer in the Transformer encoder.
* **intermediate\_size** (`int`, *optional*, defaults to 3072) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
* **hidden\_act** (`str` or `function`, *optional*, defaults to `"gelu"`) — The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
* **hidden\_dropout\_prob** (`float`, *optional*, defaults to 0.1) — The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
* **attention\_probs\_dropout\_prob** (`float`, *optional*, defaults to 0.1) — The dropout ratio for the attention probabilities.
* **initializer\_range** (`float`, *optional*, defaults to 0.02) — The standard deviation of the truncated\_normal\_initializer for initializing all weight matrices.
* **layer\_norm\_eps** (`float`, *optional*, defaults to 1e-12) — The epsilon used by the layer normalization layers.
* **qkv\_bias** (`bool`, *optional*, defaults to `True`) — Whether to add a bias to the queries, keys and values.
* **use\_cls\_token** (`bool`, *optional*, defaults to `True`) — Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model.

This is the configuration class to store the configuration of a [FlavaMultimodalModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalModel). It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture.

Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.

Configuration objects inherit from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) and can be used to control the model outputs. Read the documentation from [PretrainedConfig](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/configuration#transformers.PretrainedConfig) for more information.

Example:

Copied

```
>>> from transformers import FlavaMultimodalConfig, FlavaMultimodalModel

>>> # Initializing a FlavaMultimodalModel with  style configuration
>>> configuration = FlavaMultimodalConfig()

>>> # Initializing a FlavaMultimodalModel model (with random weights) from the style configuration
>>> model = FlavaMultimodalModel(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config
```

### FlavaImageCodebookConfig

#### class transformers.FlavaImageCodebookConfig

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/configuration_flava.py#L382)

( num\_groups: int = 4input\_channels: int = 3num\_blocks\_per\_group: int = 2hidden\_size: int = 256vocab\_size: int = 8192freeze: int = Trueinitializer\_range: float = 0.02\*\*kwargs )

### FlavaProcessor

#### class transformers.FlavaProcessor

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/processing_flava.py#L28)

( image\_processor = Nonetokenizer = None\*\*kwargs )

Parameters

* **image\_processor** ([FlavaImageProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageProcessor)) — The image processor is a required input.
* **tokenizer** ([BertTokenizerFast](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bert#transformers.BertTokenizerFast)) — The tokenizer is a required input.

Constructs a FLAVA processor which wraps a FLAVA image processor and a FLAVA tokenizer into a single processor.

[FlavaProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaProcessor) offers all the functionalities of [FlavaImageProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageProcessor) and [BertTokenizerFast](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/bert#transformers.BertTokenizerFast). See the `__call__()` and [decode()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaProcessor.decode) for more information.

**batch\_decode**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/processing_flava.py#L130)

( \*args\*\*kwargs )

This method forwards all its arguments to BertTokenizerFast’s [batch\_decode()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.batch_decode). Please refer to the docstring of this method for more information.

**decode**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/processing_flava.py#L137)

( \*args\*\*kwargs )

This method forwards all its arguments to BertTokenizerFast’s [decode()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.decode). Please refer to the docstring of this method for more information.

### FlavaFeatureExtractor

#### class transformers.FlavaFeatureExtractor

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/feature_extraction_flava.py#L26)

( \*args\*\*kwargs )

### FlavaImageProcessor

#### class transformers.FlavaImageProcessor

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/image_processing_flava.py#L135)

( do\_resize: bool = Truesize: typing.Dict\[str, int] = Noneresample: Resampling = \<Resampling.BICUBIC: 3>do\_center\_crop: bool = Truecrop\_size: typing.Dict\[str, int] = Nonedo\_rescale: bool = Truerescale\_factor: typing.Union\[int, float] = 0.00392156862745098do\_normalize: bool = Trueimage\_mean: typing.Union\[float, typing.Iterable\[float], NoneType] = Noneimage\_std: typing.Union\[float, typing.Iterable\[float], NoneType] = Nonereturn\_image\_mask: bool = Falseinput\_size\_patches: int = 14total\_mask\_patches: int = 75mask\_group\_min\_patches: int = 16mask\_group\_max\_patches: typing.Optional\[int] = Nonemask\_group\_min\_aspect\_ratio: float = 0.3mask\_group\_max\_aspect\_ratio: typing.Optional\[float] = Nonereturn\_codebook\_pixels: bool = Falsecodebook\_do\_resize: bool = Truecodebook\_size: bool = Nonecodebook\_resample: int = \<Resampling.LANCZOS: 1>codebook\_do\_center\_crop: bool = Truecodebook\_crop\_size: int = Nonecodebook\_do\_rescale: bool = Truecodebook\_rescale\_factor: typing.Union\[int, float] = 0.00392156862745098codebook\_do\_map\_pixels: bool = Truecodebook\_do\_normalize: bool = Truecodebook\_image\_mean: typing.Union\[float, typing.Iterable\[float], NoneType] = Nonecodebook\_image\_std: typing.Union\[float, typing.Iterable\[float], NoneType] = None\*\*kwargs )

Parameters

* **do\_resize** (`bool`, *optional*, defaults to `True`) — Whether to resize the image’s (height, width) dimensions to the specified `size`. Can be overridden by the `do_resize` parameter in `preprocess`.
* **size** (`Dict[str, int]` *optional*, defaults to `{"height" -- 224, "width": 224}`): Size of the image after resizing. Can be overridden by the `size` parameter in `preprocess`.
* **resample** (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`) — Resampling filter to use if resizing the image. Can be overridden by the `resample` parameter in `preprocess`.
* **do\_center\_crop** (`bool`, *optional*, defaults to `True`) — Whether to center crop the images. Can be overridden by the `do_center_crop` parameter in `preprocess`.
* **crop\_size** (`Dict[str, int]` *optional*, defaults to `{"height" -- 224, "width": 224}`): Size of image after the center crop `(crop_size["height"], crop_size["width"])`. Can be overridden by the `crop_size` parameter in `preprocess`.
* **do\_rescale** (`bool`, *optional*, defaults to `True`) — Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale` parameter in `preprocess`.
* **rescale\_factor** (`int` or `float`, *optional*, defaults to `1/255`) — Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in `preprocess`.
* **do\_normalize** (`bool`, *optional*, defaults to `True`) — Whether to normalize the image. Can be overridden by the `do_normalize` parameter in `preprocess`.
* **image\_mean** (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`) — Mean to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
* **image\_std** (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`) — Standard deviation to use if normalizing the image. This is a float or list of floats the length of the number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
* **return\_image\_mask** (`bool`, *optional*, defaults to `False`) — Whether to return the image mask. Can be overridden by the `return_image_mask` parameter in `preprocess`.
* **input\_size\_patches** (`int`, *optional*, defaults to 14) — Number of patches in the image in height and width direction. 14x14 = 196 total patches. Can be overridden by the `input_size_patches` parameter in `preprocess`.
* **total\_mask\_patches** (`int`, *optional*, defaults to 75) — Total number of patches that should be masked. Can be overridden by the `total_mask_patches` parameter in `preprocess`.
* **mask\_group\_min\_patches** (`int`, *optional*, defaults to 16) — Minimum number of patches that should be masked. Can be overridden by the `mask_group_min_patches` parameter in `preprocess`.
* **mask\_group\_max\_patches** (`int`, *optional*) — Maximum number of patches that should be masked. Can be overridden by the `mask_group_max_patches` parameter in `preprocess`.
* **mask\_group\_min\_aspect\_ratio** (`float`, *optional*, defaults to 0.3) — Minimum aspect ratio of the mask window. Can be overridden by the `mask_group_min_aspect_ratio` parameter in `preprocess`.
* **mask\_group\_max\_aspect\_ratio** (`float`, *optional*) — Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`.
* **codebook\_do\_resize** (`bool`, *optional*, defaults to `True`) — Whether to resize the input for codebook to a certain. Can be overridden by the `codebook_do_resize` parameter in `preprocess`. `codebook_size`.
* **codebook\_size** (`Dict[str, int]`, *optional*, defaults to `{"height" -- 224, "width": 224}`): Resize the input for codebook to the given size. Can be overridden by the `codebook_size` parameter in `preprocess`.
* **codebook\_resample** (`PILImageResampling`, *optional*, defaults to `PILImageResampling.LANCZOS`) — Resampling filter to use if resizing the codebook image. Can be overridden by the `codebook_resample` parameter in `preprocess`.
* **codebook\_do\_center\_crop** (`bool`, *optional*, defaults to `True`) — Whether to crop the input for codebook at the center. If the input size is smaller than `codebook_crop_size` along any edge, the image is padded with 0’s and then center cropped. Can be overridden by the `codebook_do_center_crop` parameter in `preprocess`.
* **codebook\_crop\_size** (`Dict[str, int]`, *optional*, defaults to `{"height" -- 224, "width": 224}`): Desired output size for codebook input when applying center-cropping. Can be overridden by the `codebook_crop_size` parameter in `preprocess`.
* **codebook\_do\_rescale** (`bool`, *optional*, defaults to `True`) — Whether to rescale the input for codebook by the specified scale `codebook_rescale_factor`. Can be overridden by the `codebook_do_rescale` parameter in `preprocess`.
* **codebook\_rescale\_factor** (`int` or `float`, *optional*, defaults to `1/255`) — Defines the scale factor to use if rescaling the codebook image. Can be overridden by the `codebook_rescale_factor` parameter in `preprocess`.
* **codebook\_do\_map\_pixels** (`bool`, *optional*, defaults to `True`) — Whether to map the pixel values of the codebook input to (1 - 2e)x + e. Can be overridden by the `codebook_do_map_pixels` parameter in `preprocess`.
* **codebook\_do\_normalize** (`bool`, *optional*, defaults to `True`) — Whether or not to normalize the input for codebook with `codebook_image_mean` and `codebook_image_std`. Can be overridden by the `codebook_do_normalize` parameter in `preprocess`.
* **codebook\_image\_mean** (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0, 0, 0]`) — The sequence of means for each channel, to be used when normalizing images for codebook. Can be overridden by the `codebook_image_mean` parameter in `preprocess`.
* **codebook\_image\_std** (`Optional[Union[float, Iterable[float]]]`, *optional*, defaults to `[0.5, 0.5, 0.5]`) — The sequence of standard deviations for each channel, to be used when normalizing images for codebook. Can be overridden by the `codebook_image_std` parameter in `preprocess`.

Constructs a Flava image processor.

**preprocess**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/image_processing_flava.py#L447)

( images: typing.Union\[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List\[ForwardRef('PIL.Image.Image')], typing.List\[numpy.ndarray], typing.List\[ForwardRef('torch.Tensor')]]do\_resize: typing.Optional\[bool] = Nonesize: typing.Dict\[str, int] = Noneresample: Resampling = Nonedo\_center\_crop: typing.Optional\[bool] = Nonecrop\_size: typing.Union\[typing.Dict\[str, int], NoneType] = Nonedo\_rescale: typing.Optional\[bool] = Nonerescale\_factor: typing.Optional\[float] = Nonedo\_normalize: typing.Optional\[bool] = Noneimage\_mean: typing.Union\[float, typing.List\[float], NoneType] = Noneimage\_std: typing.Union\[float, typing.List\[float], NoneType] = Nonereturn\_image\_mask: typing.Optional\[bool] = Noneinput\_size\_patches: typing.Optional\[int] = Nonetotal\_mask\_patches: typing.Optional\[int] = Nonemask\_group\_min\_patches: typing.Optional\[int] = Nonemask\_group\_max\_patches: typing.Optional\[int] = Nonemask\_group\_min\_aspect\_ratio: typing.Optional\[float] = Nonemask\_group\_max\_aspect\_ratio: typing.Optional\[float] = Nonereturn\_codebook\_pixels: typing.Optional\[bool] = Nonecodebook\_do\_resize: typing.Optional\[bool] = Nonecodebook\_size: typing.Union\[typing.Dict\[str, int], NoneType] = Nonecodebook\_resample: typing.Optional\[int] = Nonecodebook\_do\_center\_crop: typing.Optional\[bool] = Nonecodebook\_crop\_size: typing.Union\[typing.Dict\[str, int], NoneType] = Nonecodebook\_do\_rescale: typing.Optional\[bool] = Nonecodebook\_rescale\_factor: typing.Optional\[float] = Nonecodebook\_do\_map\_pixels: typing.Optional\[bool] = Nonecodebook\_do\_normalize: typing.Optional\[bool] = Nonecodebook\_image\_mean: typing.Optional\[typing.Iterable\[float]] = Nonecodebook\_image\_std: typing.Optional\[typing.Iterable\[float]] = Nonereturn\_tensors: typing.Union\[str, transformers.utils.generic.TensorType, NoneType] = Nonedata\_format: ChannelDimension = \<ChannelDimension.FIRST: 'channels\_first'>input\_data\_format: typing.Union\[str, transformers.image\_utils.ChannelDimension, NoneType] = None\*\*kwargs )

Parameters

* **images** (`ImageInput`) — Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`.
* **do\_resize** (`bool`, *optional*, defaults to `self.do_resize`) — Whether to resize the image.
* **size** (`Dict[str, int]`, *optional*, defaults to `self.size`) — Size of the image.
* **resample** (`int`, *optional*, defaults to `self.resample`) — Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`, Only has an effect if `do_resize` is set to `True`.
* **do\_center\_crop** (`bool`, *optional*, defaults to `self.do_center_crop`) — Whether to center crop the image.
* **crop\_size** (`Dict[str, int]`, *optional*, defaults to `self.crop_size`) — Size of the center crop. Only has an effect if `do_center_crop` is set to `True`.
* **do\_rescale** (`bool`, *optional*, defaults to `self.do_rescale`) — Whether to rescale the image values between \[0 - 1].
* **rescale\_factor** (`float`, *optional*, defaults to `self.rescale_factor`) — Rescale factor to rescale the image by if `do_rescale` is set to `True`.
* **do\_normalize** (`bool`, *optional*, defaults to `self.do_normalize`) — Whether to normalize the image.
* **image\_mean** (`float` or `List[float]`, *optional*, defaults to `self.image_mean`) — Image mean.
* **image\_std** (`float` or `List[float]`, *optional*, defaults to `self.image_std`) — Image standard deviation.
* **return\_image\_mask** (`bool`, *optional*, defaults to `self.return_image_mask`) — Whether to return the image mask.
* **input\_size\_patches** (`int`, *optional*, defaults to `self.input_size_patches`) — Size of the patches to extract from the image.
* **total\_mask\_patches** (`int`, *optional*, defaults to `self.total_mask_patches`) — Total number of patches to extract from the image.
* **mask\_group\_min\_patches** (`int`, *optional*, defaults to `self.mask_group_min_patches`) — Minimum number of patches to extract from the image.
* **mask\_group\_max\_patches** (`int`, *optional*, defaults to `self.mask_group_max_patches`) — Maximum number of patches to extract from the image.
* **mask\_group\_min\_aspect\_ratio** (`float`, *optional*, defaults to `self.mask_group_min_aspect_ratio`) — Minimum aspect ratio of the patches to extract from the image.
* **mask\_group\_max\_aspect\_ratio** (`float`, *optional*, defaults to `self.mask_group_max_aspect_ratio`) — Maximum aspect ratio of the patches to extract from the image.
* **return\_codebook\_pixels** (`bool`, *optional*, defaults to `self.return_codebook_pixels`) — Whether to return the codebook pixels.
* **codebook\_do\_resize** (`bool`, *optional*, defaults to `self.codebook_do_resize`) — Whether to resize the codebook pixels.
* **codebook\_size** (`Dict[str, int]`, *optional*, defaults to `self.codebook_size`) — Size of the codebook pixels.
* **codebook\_resample** (`int`, *optional*, defaults to `self.codebook_resample`) — Resampling filter to use if resizing the codebook pixels. This can be one of the enum `PILImageResampling`, Only has an effect if `codebook_do_resize` is set to `True`.
* **codebook\_do\_center\_crop** (`bool`, *optional*, defaults to `self.codebook_do_center_crop`) — Whether to center crop the codebook pixels.
* **codebook\_crop\_size** (`Dict[str, int]`, *optional*, defaults to `self.codebook_crop_size`) — Size of the center crop of the codebook pixels. Only has an effect if `codebook_do_center_crop` is set to `True`.
* **codebook\_do\_rescale** (`bool`, *optional*, defaults to `self.codebook_do_rescale`) — Whether to rescale the codebook pixels values between \[0 - 1].
* **codebook\_rescale\_factor** (`float`, *optional*, defaults to `self.codebook_rescale_factor`) — Rescale factor to rescale the codebook pixels by if `codebook_do_rescale` is set to `True`.
* **codebook\_do\_map\_pixels** (`bool`, *optional*, defaults to `self.codebook_do_map_pixels`) — Whether to map the codebook pixels values.
* **codebook\_do\_normalize** (`bool`, *optional*, defaults to `self.codebook_do_normalize`) — Whether to normalize the codebook pixels.
* **codebook\_image\_mean** (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_mean`) — Codebook pixels mean to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`.
* **codebook\_image\_std** (`float` or `List[float]`, *optional*, defaults to `self.codebook_image_std`) — Codebook pixels standard deviation to normalize the codebook pixels by if `codebook_do_normalize` is set to `True`.
* **return\_tensors** (`str` or `TensorType`, *optional*) — The type of tensors to return. Can be one of:
  * Unset: Return a list of `np.ndarray`.
  * `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
  * `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
  * `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
  * `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
* **data\_format** (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`) — The channel dimension format for the output image. Can be one of:
  * `ChannelDimension.FIRST`: image in (num\_channels, height, width) format.
  * `ChannelDimension.LAST`: image in (height, width, num\_channels) format.
* **input\_data\_format** (`ChannelDimension` or `str`, *optional*) — The channel dimension format for the input image. If unset, the channel dimension format is inferred from the input image. Can be one of:
  * `"channels_first"` or `ChannelDimension.FIRST`: image in (num\_channels, height, width) format.
  * `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num\_channels) format.
  * `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

Preprocess an image or batch of images.

### FlavaForPreTraining

#### class transformers.FlavaForPreTraining

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1727)

( config: FlavaConfigimage\_codebook: typing.Optional\[torch.nn.modules.module.Module] = None )

Parameters

* **config** ([FlavaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.
* **image\_codebook** (`nn.Module`) — If passed, the image codebook will be set to this. Otherwise. it will be initialized using the image\_codebook\_config defined in the config first as the first parameter.

The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1771)

( input\_ids: typing.Optional\[torch.LongTensor] = Noneinput\_ids\_masked: typing.Optional\[torch.LongTensor] = Nonepixel\_values: typing.Optional\[torch.FloatTensor] = Nonecodebook\_pixel\_values: typing.Optional\[torch.FloatTensor] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor] = Nonebool\_masked\_pos: typing.Optional\[torch.Tensor] = Noneposition\_ids: typing.Optional\[torch.LongTensor] = Noneimage\_attention\_mask: typing.Optional\[torch.Tensor] = Noneskip\_unmasked\_multimodal\_encoder: bool = Nonemlm\_labels: typing.Optional\[torch.Tensor] = Nonemim\_labels: typing.Optional\[torch.Tensor] = Noneitm\_labels: typing.Optional\[torch.Tensor] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: bool = Truereturn\_dict: typing.Optional\[bool] = Nonereturn\_loss: typing.Optional\[bool] = None ) → `transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput` or `tuple(torch.FloatTensor)`

Parameters

* **input\_ids\_masked** (`torch.LongTensor` of shape `(batch_size, text_seq_len)`) — Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task to be used with MLM. Indices can be obtained using [AutoTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoTokenizer) along with `DataCollatorForMaskedLanguageModeling`. See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details. [What are input IDs?](https://huggingface.co/docs/transformers/glossary#input-ids)
* **input\_ids** (`torch.LongTensor` of shape `(batch_size, text_seq_len)`) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using [AutoTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details. [What are input IDs?](https://huggingface.co/docs/transformers/glossary#input-ids)
* **token\_type\_ids** (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  * 0 corresponds to a *sentence A* token,
  * 1 corresponds to a *sentence B* token. [What are token type IDs?](https://huggingface.co/docs/transformers/glossary#token-type-ids)
* **pixel\_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using [AutoImageProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoImageProcessor). See [FlavaImageProcessor.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.
* **bool\_masked\_pos** (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
* **interpolate\_pos\_encoding** (`bool`, *optional*) — Whether to interpolate the pre-trained position encodings.
* **image\_attention\_mask** (`torch.FloatTensor` of shape `(batch_size, image_num_patches)`, *optional*) — Mask to avoid performing attention on padding token indices specifically for images. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **skip\_unmasked\_multimodal\_encoder** (*bool*, *optional*) — Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn’t need unmasked multimodal embeddings or outputs as of now.
* **mlm\_labels** (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*) — Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction). Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., text_config.vocab_size - 1]`.
* **mim\_labels** (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*) — Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ..., image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are generated automatically using the image codebook assigned to the model. By default, it uses [FlavaImageCodebook](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageCodebook). See [FlavaImageCodebook](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageCodebook) to understand how to generate mim\_labels.
* **itm\_labels** (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*) — Labels for computing the image-text matching loss. 0 means the pairs don’t match and 1 means they match. The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well.
* **return\_loss** (`bool`, *optional*, default to None) — Whether to return calculated loss or not.
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, text_seq_len)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

  Examples —

Returns

`transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.flava.modeling_flava.FlavaForPreTrainingOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`<class 'transformers.models.flava.configuration_flava.FlavaConfig'>`) and inputs.

* **loss** (`torch.FloatTensor`, *optional*, returned when `return_loss` is True) — Total loss calculated for this model.
* **loss\_info** (`FlavaLosses`) — Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on the keys.
* **image\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present) — The image embeddings which are basically the pooled output of [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel).
* **image\_output** (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present) — The output of the [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel).
* **text\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present) — The text embeddings which are basically the pooled output of [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **text\_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present) — The output of the [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **multimodal\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`) — The multimodal embeddings which are basically the pooled output of [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **multimodal\_output** (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`) — The output of the [FlavaMultimodalModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalModel).
* **image\_masked\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present) — The image embeddings which are basically the pooled output of [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel). Uses `bool_masked_pos` to create masked images.
* **image\_masked\_output** (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present) — The output of the [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel). Uses `bool_masked_pos` to create masked images.
* **text\_masked\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present) — The text embeddings which are basically the pooled output of [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **text\_masked\_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present) — The output of the [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **multimodal\_masked\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present) — The multimodal embeddings which are basically the pooled output of [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **multimodal\_masked\_output** (`BaseModelOutputWithPooling`, returned when `input_ids_masked` and `pixel_values` are present) — The output of the [FlavaMultimodalModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalModel).
* **mim\_logits** (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not) — The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked.
* **mlm\_logits** (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not) — The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked.
* **itm\_logits** (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present) — The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA.
* **mmm\_image\_logits** (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present) — The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked.
* **mmm\_text\_logits** (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total\_masked\_seq\_length, text\_vocab\_size)`), *optional*, returned when` pixel\_values`and`input\_ids\_masked`are present) -- The logits for MMM text multimodal loss. The flattened output is returned when`input\_ids\_masked\` has some of the tokens masked.
* **contrastive\_logits\_per\_image** (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`) — The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA’s `image_projection` and `text_projection` layers respectively. This represents the image-text similarity scores. This is calculated on unmasked images and texts.
* **contrastive\_logits\_per\_text** (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`) — The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA’s `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and texts.

The [FlavaForPreTraining](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaForPreTraining) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

### FlavaModel

#### class transformers.FlavaModel

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1193)

( config: FlavaConfig )

Parameters

* **config** ([FlavaConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare FLAVA Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1337)

( input\_ids: typing.Optional\[torch.LongTensor] = Nonepixel\_values: typing.Optional\[torch.FloatTensor] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor] = Nonebool\_masked\_pos: typing.Optional\[torch.Tensor] = Noneposition\_ids: typing.Optional\[torch.LongTensor] = Noneimage\_attention\_mask: typing.Optional\[torch.Tensor] = Noneskip\_multimodal\_encoder: typing.Optional\[bool] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: bool = Truereturn\_dict: typing.Optional\[bool] = None ) → `transformers.models.flava.modeling_flava.FlavaModelOutput` or `tuple(torch.FloatTensor)`

Parameters

* **pixel\_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using [AutoImageProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoImageProcessor). See [FlavaImageProcessor.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.
* **bool\_masked\_pos** (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
* **interpolate\_pos\_encoding** (`bool`, *optional*) — Whether to interpolate the pre-trained position encodings.
* **input\_ids** (`torch.LongTensor` of shape `(batch_size, image_num_patches + text_seq_len)`) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using [AutoTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details. [What are input IDs?](https://huggingface.co/docs/transformers/glossary#input-ids)
* **token\_type\_ids** (`torch.LongTensor` of shape `(batch_size, image_num_patches + text_seq_len)`, *optional*) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  * 0 corresponds to a *sentence A* token,
  * 1 corresponds to a *sentence B* token. [What are token type IDs?](https://huggingface.co/docs/transformers/glossary#token-type-ids)
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.
* **skip\_multimodal\_encoder** (*bool*, *optional*) — Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used.

Returns

`transformers.models.flava.modeling_flava.FlavaModelOutput` or `tuple(torch.FloatTensor)`

A `transformers.models.flava.modeling_flava.FlavaModelOutput` or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (`<class 'transformers.models.flava.configuration_flava.FlavaConfig'>`) and inputs.

* **image\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present) — The image embeddings which are basically the pooled output of [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel).
* **image\_output** (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present) — The output of the [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel).
* **text\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present) — The text embeddings which are basically the pooled output of [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **text\_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present) — The output of the [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **multimodal\_embeddings** (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`) — The multimodal embeddings which are basically the pooled output of [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel).
* **multimodal\_output** (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`) — The output of the [FlavaMultimodalModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalModel).

The [FlavaModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Examples:

Copied

```
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, FlavaModel

>>> model = FlavaModel.from_pretrained("facebook/flava-full")
>>> processor = AutoProcessor.from_pretrained("facebook/flava-full")

>>> 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"], images=image, return_tensors="pt", padding=True)

>>> outputs = model(**inputs)
>>> logits_per_image = outputs.contrastive_logits_per_image  # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
```

**get\_text\_features**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1239)

( input\_ids: typing.Optional\[torch.Tensor] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor] = Noneposition\_ids: typing.Optional\[torch.Tensor] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None )

Parameters

* **input\_ids** (`torch.LongTensor` of shape `(batch_size, text_seq_length)`) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using [AutoTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details. [What are input IDs?](https://huggingface.co/docs/transformers/glossary#input-ids)
* **token\_type\_ids** (`torch.LongTensor` of shape `(batch_size, text_seq_length)`, *optional*) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  * 0 corresponds to a *sentence A* token,
  * 1 corresponds to a *sentence B* token. [What are token type IDs?](https://huggingface.co/docs/transformers/glossary#token-type-ids)
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, text_seq_length)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

The [FlavaModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

**get\_image\_features**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1285)

( pixel\_values: typing.Optional\[torch.Tensor] = Nonebool\_masked\_pos: typing.Optional\[torch.BoolTensor] = Noneinterpolate\_pos\_encoding: typing.Optional\[bool] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonehead\_mask: typing.Optional\[torch.Tensor] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None )

Parameters

* **pixel\_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using [AutoImageProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoImageProcessor). See [FlavaImageProcessor.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.
* **bool\_masked\_pos** (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
* **interpolate\_pos\_encoding** (`bool`, *optional*) — Whether to interpolate the pre-trained position encodings.
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, image_num_patches)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

The [FlavaModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

### FlavaImageCodebook

#### class transformers.FlavaImageCodebook

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1511)

( config: FlavaImageCodebookConfig\*\*kwargs: typing.Any )

Parameters

* **config** ([FlavaImageCodebookConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageCodebookConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The FLAVA’s image codebook model inspired from DALL-E’s original encoder. Outputs raw hidden states and can be used to generate image tokens for an image based on DALL-E’s vocab. Used to generate labels for MIM. Use `get_codebook_indices` to get image tokens for an image.

This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1595)

( pixel\_values: FloatTensor )

**get\_codebook\_indices**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1561)

( pixel\_values: Tensor )

**get\_codebook\_probs**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1591)

( pixel\_values: Tensor )

### FlavaTextModel

#### class transformers.FlavaTextModel

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L990)

( config: FlavaTextConfigadd\_pooling\_layer: bool = True )

Parameters

* **config** ([FlavaTextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare FLAVA Text Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1021)

( input\_ids: typing.Optional\[torch.Tensor] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonetoken\_type\_ids: typing.Optional\[torch.Tensor] = Noneposition\_ids: typing.Optional\[torch.Tensor] = Nonehead\_mask: typing.Optional\[torch.Tensor] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None ) → [transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

Parameters

* **input\_ids** (`torch.LongTensor` of shape `(batch_size, text_seq_length)`) — Indices of input sequence tokens in the vocabulary. Indices can be obtained using [AutoTokenizer](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode) and [PreTrainedTokenizer.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/vits#transformers.VitsTokenizer.__call__) for details. [What are input IDs?](https://huggingface.co/docs/transformers/glossary#input-ids)
* **token\_type\_ids** (`torch.LongTensor` of shape `(batch_size, text_seq_length)`, *optional*) — Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
  * 0 corresponds to a *sentence A* token,
  * 1 corresponds to a *sentence B* token. [What are token type IDs?](https://huggingface.co/docs/transformers/glossary#token-type-ids)
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, text_seq_length)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

Returns

[transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

A [transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([FlavaTextConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextConfig)) and inputs.

* **last\_hidden\_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.
* **pooler\_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
* **hidden\_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
* **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The [FlavaTextModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaTextModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

```
>>> from transformers import AutoTokenizer, FlavaTextModel
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/flava-full")
>>> model = FlavaTextModel.from_pretrained("facebook/flava-full")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
```

### FlavaImageModel

#### class transformers.FlavaImageModel

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L891)

( config: FlavaImageConfigadd\_pooling\_layer: bool = True )

Parameters

* **config** ([FlavaImageConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L924)

( pixel\_values: typing.Optional\[torch.Tensor] = Nonebool\_masked\_pos: typing.Optional\[torch.BoolTensor] = Noneinterpolate\_pos\_encoding: typing.Optional\[bool] = Noneattention\_mask: typing.Optional\[torch.Tensor] = Nonehead\_mask: typing.Optional\[torch.Tensor] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None ) → [transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

Parameters

* **pixel\_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using [AutoImageProcessor](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/auto#transformers.AutoImageProcessor). See [FlavaImageProcessor.**call**()](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/deit#transformers.DeiTFeatureExtractor.__call__) for details.
* **bool\_masked\_pos** (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`) — Boolean masked positions. Indicates which patches are masked (1) and which aren’t (0).
* **interpolate\_pos\_encoding** (`bool`, *optional*) — Whether to interpolate the pre-trained position encodings.
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, image_num_patches)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

Returns

[transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

A [transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([FlavaImageConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageConfig)) and inputs.

* **last\_hidden\_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.
* **pooler\_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
* **hidden\_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
* **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The [FlavaImageModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaImageModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

```
>>> from transformers import AutoImageProcessor, FlavaImageModel
>>> import torch
>>> from datasets import load_dataset

>>> dataset = load_dataset("boincai/cats-image")
>>> image = dataset["test"]["image"][0]

>>> image_processor = AutoImageProcessor.from_pretrained("facebook/flava-full")
>>> model = FlavaImageModel.from_pretrained("facebook/flava-full")

>>> inputs = image_processor(image, return_tensors="pt")

>>> with torch.no_grad():
...     outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
>>> list(last_hidden_states.shape)
[1, 197, 768]
```

### FlavaMultimodalModel

#### class transformers.FlavaMultimodalModel

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1095)

( config: FlavaMultimodalConfigadd\_pooling\_layer = True )

Parameters

* **config** ([FlavaMultimodalConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalConfig)) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [from\_pretrained()](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights.

The bare FLAVA Multimodal Model transformer outputting raw hidden-states without any specific head on top. This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

**forward**

[\<source>](https://github.com/huggingface/transformers/blob/v4.34.1/src/transformers/models/flava/modeling_flava.py#L1123)

( hidden\_states: Tensorattention\_mask: typing.Optional\[torch.Tensor] = Nonehead\_mask: typing.Optional\[torch.Tensor] = Noneoutput\_attentions: typing.Optional\[bool] = Noneoutput\_hidden\_states: typing.Optional\[bool] = Nonereturn\_dict: typing.Optional\[bool] = None ) → [transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

Parameters

* **hidden\_states** (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`) — The concatenated hidden states of unimodal encoders.
* **attention\_mask** (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len)`, *optional*) — Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  * 1 for tokens that are **not masked**,
  * 0 for tokens that are **masked**. [What are attention masks?](https://huggingface.co/docs/transformers/glossary#attention-mask)
* **head\_mask** (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*) — Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  * 1 indicates the head is **not masked**,
  * 0 indicates the head is **masked**.
* **output\_attentions** (`bool`, *optional*) — Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail.
* **output\_hidden\_states** (`bool`, *optional*) — Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail.
* **return\_dict** (`bool`, *optional*) — Whether or not to return a [ModelOutput](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.

Returns

[transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`

A [transformers.modeling\_outputs.BaseModelOutputWithPooling](https://huggingface.co/docs/transformers/v4.34.1/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration ([FlavaMultimodalConfig](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalConfig)) and inputs.

* **last\_hidden\_state** (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`) — Sequence of hidden-states at the output of the last layer of the model.
* **pooler\_output** (`torch.FloatTensor` of shape `(batch_size, hidden_size)`) — Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
* **hidden\_states** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
* **attentions** (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The [FlavaMultimodalModel](https://huggingface.co/docs/transformers/v4.34.1/en/model_doc/flava#transformers.FlavaMultimodalModel) forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

Copied

```
>>> from transformers import AutoTokenizer, FlavaMultimodalModel
>>> import torch

>>> tokenizer = AutoTokenizer.from_pretrained("facebook/flava-full")
>>> model = FlavaMultimodalModel.from_pretrained("facebook/flava-full")

>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)

>>> last_hidden_states = outputs.last_hidden_state
```


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