#
FLAVA

##
Overview

The FLAVA model was proposed in FLAVA: A Foundational Language And Vision Alignment Model 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. The original code can be found here.

##
FlavaConfig

###
class transformers.FlavaConfig

< source >

( image_config: typing.Dict[str, typing.Any] = None text_config: typing.Dict[str, typing.Any] = None multimodal_config: typing.Dict[str, typing.Any] = None image_codebook_config: typing.Dict[str, typing.Any] = None hidden_size: int = 768 layer_norm_eps: float = 1e-12 projection_dim: int = 768 init_codebook: bool = True logit_scale_init_value: float = 2.6592 initializer_range: float = 0.02 ce_ignore_index: int = -100 mim_weight: float = 1.0 mlm_weight: float = 1.0 global_contrastive_weight: float = 1.0 itm_weight: float = 1.0 mmm_image_weight: float = 1.0 mmm_text_weight: float = 1.0 global_backprop_contrastive: bool = True skip_unmasked_multimodal_encoder: bool = True return_loss: bool = True **kwargs )

Parameters

- text_config (`dict`, optional) — Dictionary of configuration options used to initialize FlavaTextConfig.
- image_config (`dict`, optional) — Dictionary of configuration options used to initialize FlavaImageConfig.
- multimodal_config (`dict`, optional) — Dictionary of configuration options used to initialize 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) — Dimensionality of text and image projection layers.
- logit_scale_init_value (`float`, optional, defaults to 2.6592) — The initial value of the logit_scale parameter. 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 is the configuration class to store the configuration of a 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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> 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 >

( image_config: FlavaImageConfig text_config: FlavaTextConfig multimodal_config: FlavaMultimodalConfig image_codebook_config: FlavaImageCodebookConfig **kwargs ) →
FlavaConfig

Returns

FlavaConfig

An instance of a configuration object

Instantiate a 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 >

( vocab_size: int = 30522 type_vocab_size: int = 2 max_position_embeddings: int = 512 position_embedding_type: str = 'absolute' hidden_size: int = 768 num_hidden_layers: int = 12 num_attention_heads: int = 12 intermediate_size: int = 3072 hidden_act: str = 'gelu' hidden_dropout_prob: float = 0.0 attention_probs_dropout_prob: float = 0.0 initializer_range: float = 0.02 layer_norm_eps: float = 1e-12 pad_token_id: int = 0 qkv_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.
- type_vocab_size (`int`, optional, defaults to 2) — The vocabulary size of the `token_type_ids` passed when calling 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.). For more information on `"relative_key_query"`, please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.).
- 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 probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`float`, optional, defaults to 0.1) — The dropout ratio for the attention probabilities.
- 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. 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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> 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 >

( hidden_size: int = 768 num_hidden_layers: int = 12 num_attention_heads: int = 12 intermediate_size: int = 3072 hidden_act: int = 'gelu' hidden_dropout_prob: float = 0.0 attention_probs_dropout_prob: float = 0.0 initializer_range: float = 0.02 layer_norm_eps: float = 1e-12 image_size: int = 224 patch_size: int = 16 num_channels: int = 3 qkv_bias: bool = True mask_token: bool = True vocab_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 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`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.
- 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 used in conjunction with FlavaImageModel for MIM (Masked Image Modeling) loss for FLAVA.

This is the configuration class to store the configuration of a 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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> 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 >

( hidden_size: int = 768 num_hidden_layers: int = 6 num_attention_heads: int = 12 intermediate_size: int = 3072 hidden_act: int = 'gelu' hidden_dropout_prob: int = 0.0 attention_probs_dropout_prob: int = 0.0 initializer_range: float = 0.02 layer_norm_eps: float = 1e-12 qkv_bias: bool = True use_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 6) — 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 0.0) — The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob (`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.
- 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. 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 architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Example:

    >>> 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 >

( num_groups: int = 4 input_channels: int = 3 num_blocks_per_group: int = 2 hidden_size: int = 256 vocab_size: int = 8192 freeze: int = True initializer_range: float = 0.02 **kwargs )

##
FlavaProcessor

###
class transformers.FlavaProcessor

< source >

( image_processor = None tokenizer = None **kwargs )

Parameters

- image_processor (FlavaImageProcessor, optional) — The image processor is a required input.
- tokenizer (BertTokenizerFast, optional) — 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 offers all the functionalities of FlavaImageProcessor and BertTokenizerFast. See the `__call__()` and decode() for more information.

####
batch_decode

< source >

( *args **kwargs )

This method forwards all its arguments to BertTokenizerFast’s batch_decode(). Please refer to the docstring of this method for more information.

####
decode

< source >

( *args **kwargs )

This method forwards all its arguments to BertTokenizerFast’s decode(). Please refer to the docstring of this method for more information.

##
FlavaFeatureExtractor

###
class transformers.FlavaFeatureExtractor

< source >

( *args **kwargs )

##
FlavaImageProcessor

###
class transformers.FlavaImageProcessor

< source >

( do_resize: bool = True size: typing.Dict[str, int] = None resample: Resampling = <Resampling.BICUBIC: 3> do_center_crop: bool = True crop_size: typing.Dict[str, int] = None do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, typing.Iterable[float], NoneType] = None image_std: typing.Union[float, typing.Iterable[float], NoneType] = None return_image_mask: bool = False input_size_patches: int = 14 total_mask_patches: int = 75 mask_group_min_patches: int = 16 mask_group_max_patches: typing.Optional[int] = None mask_group_min_aspect_ratio: float = 0.3 mask_group_max_aspect_ratio: typing.Optional[float] = None return_codebook_pixels: bool = False codebook_do_resize: bool = True codebook_size: bool = None codebook_resample: int = <Resampling.LANCZOS: 1> codebook_do_center_crop: bool = True codebook_crop_size: int = None codebook_do_rescale: bool = True codebook_rescale_factor: typing.Union[int, float] = 0.00392156862745098 codebook_do_map_pixels: bool = True codebook_do_normalize: bool = True codebook_image_mean: typing.Union[float, typing.Iterable[float], NoneType] = None codebook_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 >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_resize: typing.Optional[bool] = None size: typing.Dict[str, int] = None resample: Resampling = None do_center_crop: typing.Optional[bool] = None crop_size: typing.Optional[typing.Dict[str, int]] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, typing.List[float], NoneType] = None image_std: typing.Union[float, typing.List[float], NoneType] = None return_image_mask: typing.Optional[bool] = None input_size_patches: typing.Optional[int] = None total_mask_patches: typing.Optional[int] = None mask_group_min_patches: typing.Optional[int] = None mask_group_max_patches: typing.Optional[int] = None mask_group_min_aspect_ratio: typing.Optional[float] = None mask_group_max_aspect_ratio: typing.Optional[float] = None return_codebook_pixels: typing.Optional[bool] = None codebook_do_resize: typing.Optional[bool] = None codebook_size: typing.Optional[typing.Dict[str, int]] = None codebook_resample: typing.Optional[int] = None codebook_do_center_crop: typing.Optional[bool] = None codebook_crop_size: typing.Optional[typing.Dict[str, int]] = None codebook_do_rescale: typing.Optional[bool] = None codebook_rescale_factor: typing.Optional[float] = None codebook_do_map_pixels: typing.Optional[bool] = None codebook_do_normalize: typing.Optional[bool] = None codebook_image_mean: typing.Optional[typing.Iterable[float]] = None codebook_image_std: typing.Optional[typing.Iterable[float]] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: ChannelDimension = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )

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 >

( config: FlavaConfig image_codebook: typing.Optional[torch.nn.modules.module.Module] = None )

Parameters

- config (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() 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 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 >

( input_ids: typing.Optional[torch.LongTensor] = None input_ids_masked: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None codebook_pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None bool_masked_pos: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None image_attention_mask: typing.Optional[torch.Tensor] = None skip_unmasked_multimodal_encoder: bool = None mlm_labels: typing.Optional[torch.Tensor] = None mim_labels: typing.Optional[torch.Tensor] = None itm_labels: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: bool = True return_dict: typing.Optional[bool] = None return_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 along with `DataCollatorForMaskedLanguageModeling`. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are 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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are 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?
- pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`) — Pixel values. Pixel values can be obtained using AutoImageProcessor. See FlavaImageProcessor.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?
- 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. See 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?
- 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 instead of a plain tuple.
- Examples —
- ```python —

  > > > from PIL import Image import requests from transformers import FlavaForPreTraining, AutoProcessor

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.

- image_output (`BaseModelOutputWithPooling`, optional, returned when `pixel_values` are present) — The output of the 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.

- text_output (`BaseModelOutputWithPooling`, optional, returned when `input_ids` are present) — The output of the 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.

- 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.

- 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. Uses `bool_masked_pos` to create masked images.

- image_masked_output (`BaseModelOutputWithPooling`, optional, returned when `pixel_values` are present) — The output of the 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.

- text_masked_output (`BaseModelOutputWithPooling`, optional, returned when `input_ids_masked` are present) — The output of the 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.

- multimodal_masked_output (`BaseModelOutputWithPooling`, optional, returned when `input_ids_masked` and `pixel_values` are present) — The output of the 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 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 >

( config: FlavaConfig )

Parameters

- config (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() 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 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 >

( input_ids: typing.Optional[torch.LongTensor] = None pixel_values: typing.Optional[torch.FloatTensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None bool_masked_pos: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None image_attention_mask: typing.Optional[torch.Tensor] = None skip_multimodal_encoder: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: bool = True return_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. See FlavaImageProcessor.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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are 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?
- 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?
- 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 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.
- image_output (`BaseModelOutputWithPooling`, optional, returned when `pixel_values` are present) — The output of the 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.
- text_output (`BaseModelOutputWithPooling`, optional, returned when `input_ids` are present) — The output of the 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.
- 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.

The 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:

    >>> 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)

    >>> image_embeddings = outputs.image_embeddings
    >>> text_embeddings = outputs.text_embeddings
    >>> multimodal_embeddings = outputs.multimodal_embeddings

    >>> outputs.image_embeddings.shape
    torch.Size([1, 197, 768])

    >>> text_embeddings.shape
    torch.Size([1, 7, 768])

    >>> multimodal_embeddings.shape
    torch.Size([1, 205, 768])

####
get_text_features

< source >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are 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?
- 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?
- 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 instead of a plain tuple.

The 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 >

( pixel_values: typing.Optional[torch.Tensor] = None bool_masked_pos: typing.Optional[torch.BoolTensor] = None interpolate_pos_encoding: typing.Optional[bool] = None attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_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. See FlavaImageProcessor.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?
- 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 instead of a plain tuple.

The 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 >

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

Parameters

- config (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() 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 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 >

( pixel_values: FloatTensor )

####
get_codebook_indices

< source >

( pixel_values: Tensor )

####
get_codebook_probs

< source >

( pixel_values: Tensor )

##
FlavaTextModel

###
class transformers.FlavaTextModel

< source >

( config: FlavaTextConfig add_pooling_layer: bool = True )

Parameters

- config (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() 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 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 >

( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
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. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details. What are 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?
- 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?
- 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 instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A 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) 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 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:

    >>> 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 >

( config: FlavaImageConfig add_pooling_layer: bool = True )

Parameters

- config (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() 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 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 >

( pixel_values: typing.Optional[torch.Tensor] = None bool_masked_pos: typing.Optional[torch.BoolTensor] = None interpolate_pos_encoding: typing.Optional[bool] = None attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
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. See FlavaImageProcessor.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?
- 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 instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A 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) 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 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:

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

    >>> dataset = load_dataset("huggingface/cats-image", trust_remote_code=True)
    >>> 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 >

( config: FlavaMultimodalConfig add_pooling_layer = True )

Parameters

- config (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() 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 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 >

( hidden_states: Tensor attention_mask: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) →
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?
- 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 instead of a plain tuple.

Returns

transformers.modeling_outputs.BaseModelOutputWithPooling or `tuple(torch.FloatTensor)`

A 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) 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 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:

    >>> 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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