# FLAVA ## Overview The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://huggingface.co/papers/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[[transformers.FlavaConfig]] #### transformers.FlavaConfig[[transformers.FlavaConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/configuration_flava.py#L183) This is the configuration class to store the configuration of a FlavaModel. It is used to instantiate a 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 [facebook/flava-full](https://huggingface.co/facebook/flava-full) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **Parameters:** image_config (`dict`, *optional*) : Dictionary of configuration options used to initialize [FlavaImageConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageConfig). text_config (`Union[dict[str, Any], ~configuration_utils.PreTrainedConfig]`, *optional*) : The config object or dictionary of the text backbone. multimodal_config (`dict`, *optional*) : Dictionary of configuration options used to initialize [FlavaMultimodalConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaMultimodalConfig). image_codebook_config (`dict`, *optional*) : Dictionary of configuration options used to initialize `FlavaCodebookConfig`. hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations. layer_norm_eps (`float`, *optional*, defaults to `1e-12`) : The epsilon used by the layer normalization layers. projection_dim (`int`, *optional*, defaults to `768`) : Dimensionality of text and vision projection layers. init_codebook (`bool`, *optional*, defaults to `True`) : Whether to initialize the codebook 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 tie_word_embeddings (`bool`, *optional*, defaults to `True`) : Whether to tie weight embeddings according to model's `tied_weights_keys` mapping. initializer_factor (`float`, *optional*, defaults to `1.0`) : A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). ## FlavaTextConfig[[transformers.FlavaTextConfig]] #### transformers.FlavaTextConfig[[transformers.FlavaTextConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/configuration_flava.py#L71) This is the configuration class to store the configuration of a FlavaModel. It is used to instantiate a 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 [facebook/flava-full](https://huggingface.co/facebook/flava-full) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **Parameters:** vocab_size (`int`, *optional*, defaults to `30522`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`. type_vocab_size (`int`, *optional*, defaults to `2`) : The vocabulary size of the `token_type_ids`. max_position_embeddings (`int`, *optional*, defaults to `512`) : The maximum sequence length that this model might ever be used with. hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder. intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`Union[float, int]`, *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. pad_token_id (`int`, *optional*, defaults to `0`) : Token id used for padding in the vocabulary. qkv_bias (`bool`, *optional*, defaults to `True`) : Whether to add a bias to the queries, keys and values. ## FlavaImageConfig[[transformers.FlavaImageConfig]] #### transformers.FlavaImageConfig[[transformers.FlavaImageConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/configuration_flava.py#L29) This is the configuration class to store the configuration of a FlavaModel. It is used to instantiate a 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 [facebook/flava-full](https://huggingface.co/facebook/flava-full) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **Parameters:** hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to `12`) : Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder. intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`Union[float, int]`, *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 (`Union[int, list[int], tuple[int, int]]`, *optional*, defaults to `224`) : The size (resolution) of each image. patch_size (`Union[int, list[int], tuple[int, 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 model. Defines the number of different tokens that can be represented by the `input_ids`. ## FlavaMultimodalConfig[[transformers.FlavaMultimodalConfig]] #### transformers.FlavaMultimodalConfig[[transformers.FlavaMultimodalConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/configuration_flava.py#L109) This is the configuration class to store the configuration of a FlavaModel. It is used to instantiate a 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 [facebook/flava-full](https://huggingface.co/facebook/flava-full) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> 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 ``` **Parameters:** hidden_size (`int`, *optional*, defaults to `768`) : Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to `6`) : Number of hidden layers in the Transformer decoder. num_attention_heads (`int`, *optional*, defaults to `12`) : Number of attention heads for each attention layer in the Transformer decoder. intermediate_size (`int`, *optional*, defaults to `3072`) : Dimension of the MLP representations. hidden_act (`str`, *optional*, defaults to `gelu`) : The non-linear activation function (function or string) in the decoder. For example, `"gelu"`, `"relu"`, `"silu"`, etc. hidden_dropout_prob (`Union[float, int]`, *optional*, defaults to `0.0`) : The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`Union[float, int]`, *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. ## FlavaImageCodebookConfig[[transformers.FlavaImageCodebookConfig]] #### transformers.FlavaImageCodebookConfig[[transformers.FlavaImageCodebookConfig]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/configuration_flava.py#L147) This is the configuration class to store the configuration of a FlavaModel. It is used to instantiate a 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 [facebook/flava-full](https://huggingface.co/facebook/flava-full) Configuration objects inherit from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) and can be used to control the model outputs. Read the documentation from [PreTrainedConfig](/docs/transformers/v5.7.0/en/main_classes/configuration#transformers.PreTrainedConfig) for more information. Example: ```python >>> from transformers import FlavaImageCodebookConfig, FlavaImageCodebook >>> # Initializing a FlavaImageCodebook with style configuration >>> configuration = FlavaImageCodebookConfig() >>> # Initializing a FlavaImageCodebook model (with random weights) from the style configuration >>> model = FlavaImageCodebook(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` **Parameters:** num_groups (`int`, *optional*, defaults to 4) : Number of groups to be created. This parameter as of now doesn't affect the model and is used for some internal calculation and estimations. input_channels (`int`, *optional*, defaults to `3`) : The number of input channels. num_blocks_per_group (`int`, *optional*, defaults to 2) : Number of conv-based blocks per group. hidden_size (`int`, *optional*, defaults to `256`) : Dimension of the hidden representations. vocab_size (`int`, *optional*, defaults to `8192`) : Vocabulary size of the model. Defines the number of different tokens that can be represented by the `input_ids`. freeze (`bool`, defaults to `True`) : Whether to freeze the weights of the model. initializer_range (`float`, *optional*, defaults to `0.02`) : The standard deviation of the truncated_normal_initializer for initializing all weight matrices. ## FlavaProcessor[[transformers.FlavaProcessor]] #### transformers.FlavaProcessor[[transformers.FlavaProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/processing_flava.py#L23) Constructs a FlavaProcessor which wraps a image processor and a tokenizer into a single processor. [FlavaProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaProcessor) offers all the functionalities of [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor) and [BertTokenizer](/docs/transformers/v5.7.0/en/model_doc/lxmert#transformers.BertTokenizer). See the [~FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor) and [~BertTokenizer](/docs/transformers/v5.7.0/en/model_doc/lxmert#transformers.BertTokenizer) for more information. __call__transformers.FlavaProcessor.__call__https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/processing_utils.py#L631[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor'], NoneType] = None"}, {"name": "text", "val": ": str | list[str] | list[list[str]] | None = None"}, {"name": "videos", "val": ": typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]], NoneType] = None"}, {"name": "audio", "val": ": typing.Union[numpy.ndarray, ForwardRef('torch.Tensor'), collections.abc.Sequence[numpy.ndarray], collections.abc.Sequence['torch.Tensor'], NoneType] = None"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.processing_utils.ProcessingKwargs]"}]- **images** (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`) -- The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. - **text** (`TextInput`, `PreTokenizedInput`, `list[TextInput]`, `list[PreTokenizedInput]`, *optional*) -- The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). - **videos** (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`) -- The video or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. - **audio** (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`) -- The audio or batch of audio to be prepared. Each audio can be a NumPy array or PyTorch tensor. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.7.0/en/internal/file_utils#transformers.TensorType), *optional*) -- If set, will return tensors of a particular framework. Acceptable values are: - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects.0[BatchFeature](/docs/transformers/v5.7.0/en/main_classes/image_processor#transformers.BatchFeature)A [BatchFeature](/docs/transformers/v5.7.0/en/main_classes/image_processor#transformers.BatchFeature) object with processed inputs in a dict format. Main method to prepare for model inputs. This method forwards the each modality argument to its own processor along with `kwargs`. Please refer to the docstring of the each processor attributes for more information. **Parameters:** image_processor (`FlavaImageProcessor`) : The image processor is a required input. tokenizer (`BertTokenizer`) : The tokenizer is a required input. **Returns:** `[BatchFeature](/docs/transformers/v5.7.0/en/main_classes/image_processor#transformers.BatchFeature)` A [BatchFeature](/docs/transformers/v5.7.0/en/main_classes/image_processor#transformers.BatchFeature) object with processed inputs in a dict format. ## FlavaImageProcessor[[transformers.FlavaImageProcessor]] #### transformers.FlavaImageProcessor[[transformers.FlavaImageProcessor]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/image_processing_flava.py#L214) Constructs a FlavaImageProcessor image processor. preprocesstransformers.FlavaImageProcessor.preprocesshttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/image_processing_flava.py#L253[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.flava.image_processing_flava.FlavaImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) -- 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`. - **return_image_mask** (`bool`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *optional*) -- Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`. - **return_codebook_pixels** (`bool`, *kwargs*, *optional*, defaults to `False`) -- Whether to return the codebook pixel values. - **codebook_do_resize** (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *optional*, defaults to `PILImageResampling.LANCZOS` for PIL backend, -- `PILImageResampling.BICUBIC` for torchvision backend): Resampling filter to use if resizing the codebook image. LANCZOS is not supported for torch Tensors; BICUBIC is used as the closest alternative for the torchvision backend. Can be overridden by the `codebook_resample` parameter in `preprocess`. - **codebook_do_center_crop** (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *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`, *kwargs* 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`, *kwargs*, *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`, *kwargs*, *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, *kwargs*, 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, *kwargs*, 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`. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.7.0/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.7.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** return_image_mask (`bool`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *optional*) : Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`. return_codebook_pixels (`bool`, *kwargs*, *optional*, defaults to `False`) : Whether to return the codebook pixel values. codebook_do_resize (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *optional*, defaults to `PILImageResampling.LANCZOS` for PIL backend, : `PILImageResampling.BICUBIC` for torchvision backend): Resampling filter to use if resizing the codebook image. LANCZOS is not supported for torch Tensors; BICUBIC is used as the closest alternative for the torchvision backend. Can be overridden by the `codebook_resample` parameter in `preprocess`. codebook_do_center_crop (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *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`, *kwargs* 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`, *kwargs*, *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`, *kwargs*, *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, *kwargs*, 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, *kwargs*, 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`. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.7.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~image_processing_base.BatchFeature`` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. ## FlavaImageProcessorPil[[transformers.FlavaImageProcessorPil]] #### transformers.FlavaImageProcessorPil[[transformers.FlavaImageProcessorPil]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/image_processing_pil_flava.py#L228) Constructs a FlavaImageProcessor image processor. preprocesstransformers.FlavaImageProcessorPil.preprocesshttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/image_processing_pil_flava.py#L265[{"name": "images", "val": ": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']]"}, {"name": "**kwargs", "val": ": typing_extensions.Unpack[transformers.models.flava.image_processing_pil_flava.FlavaImageProcessorKwargs]"}]- **images** (`Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list[PIL.Image.Image], list[numpy.ndarray], list[torch.Tensor]]`) -- 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`. - **return_image_mask** (`bool`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *optional*) -- Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`. - **return_codebook_pixels** (`bool`, *kwargs*, *optional*, defaults to `False`) -- Whether to return the codebook pixel values. - **codebook_do_resize** (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *optional*, defaults to `PILImageResampling.LANCZOS` for PIL backend, -- `PILImageResampling.BICUBIC` for torchvision backend): Resampling filter to use if resizing the codebook image. LANCZOS is not supported for torch Tensors; BICUBIC is used as the closest alternative for the torchvision backend. Can be overridden by the `codebook_resample` parameter in `preprocess`. - **codebook_do_center_crop** (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *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`, *kwargs* 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`, *kwargs*, *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`, *kwargs*, *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, *kwargs*, 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, *kwargs*, 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`. - **return_tensors** (`str` or [TensorType](/docs/transformers/v5.7.0/en/internal/file_utils#transformers.TensorType), *optional*) -- Returns stacked tensors if set to `'pt'`, otherwise returns a list of tensors. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.7.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) -- Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments.0`~image_processing_base.BatchFeature`- **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. **Parameters:** return_image_mask (`bool`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *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`, *kwargs*, *optional*) : Maximum aspect ratio of the mask window. Can be overridden by the `mask_group_max_aspect_ratio` parameter in `preprocess`. return_codebook_pixels (`bool`, *kwargs*, *optional*, defaults to `False`) : Whether to return the codebook pixel values. codebook_do_resize (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *optional*, defaults to `PILImageResampling.LANCZOS` for PIL backend, : `PILImageResampling.BICUBIC` for torchvision backend): Resampling filter to use if resizing the codebook image. LANCZOS is not supported for torch Tensors; BICUBIC is used as the closest alternative for the torchvision backend. Can be overridden by the `codebook_resample` parameter in `preprocess`. codebook_do_center_crop (`bool`, *kwargs*, *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, *kwargs*, 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`, *kwargs*, *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`, *kwargs* 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`, *kwargs*, *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`, *kwargs*, *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, *kwargs*, 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, *kwargs*, 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`. - ****kwargs** ([ImagesKwargs](/docs/transformers/v5.7.0/en/main_classes/processors#transformers.ImagesKwargs), *optional*) : Additional image preprocessing options. Model-specific kwargs are listed above; see the TypedDict class for the complete list of supported arguments. **Returns:** ``~image_processing_base.BatchFeature`` - **data** (`dict`) -- Dictionary of lists/arrays/tensors returned by the __call__ method ('pixel_values', etc.). - **tensor_type** (`Union[None, str, TensorType]`, *optional*) -- You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization. ## FlavaForPreTraining[[transformers.FlavaForPreTraining]] #### transformers.FlavaForPreTraining[[transformers.FlavaForPreTraining]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1511) The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs. This model inherits from [PreTrainedModel](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also 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. forwardtransformers.FlavaForPreTraining.forwardhttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1560[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "input_ids_masked", "val": ": torch.LongTensor | None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "codebook_pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor | None = None"}, {"name": "bool_masked_pos", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "image_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "skip_unmasked_multimodal_encoder", "val": ": bool | None = None"}, {"name": "mlm_labels", "val": ": torch.Tensor | None = None"}, {"name": "mim_labels", "val": ": torch.Tensor | None = None"}, {"name": "itm_labels", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool = True"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "return_loss", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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](/docs/transformers/v5.7.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **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](/docs/transformers/v5.7.0/en/model_doc/auto#transformers.AutoTokenizer) along with `DataCollatorForMaskedLanguageModeling`. See [PreTrainedTokenizer.encode()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor). See `FlavaImageProcessor.__call__()` for details ([FlavaProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaProcessor) uses [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor) for processing images). - **codebook_pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_image_patches, patch_size, patch_size, 3)`, *optional*) -- Pixel values for image patches that are used to compute the image codebook labels for masked image modeling. - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) - **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?](../glossary#token-type-ids) - **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). - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **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?](../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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageCodebook). See [FlavaImageCodebook](/docs/transformers/v5.7.0/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. - **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*, defaults to `True`) -- 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](/docs/transformers/v5.7.0/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple. - **return_loss** (`bool`, *optional*, default to None) -- Whether to return calculated loss or not.0`FlavaForPreTrainingOutput` or `tuple(torch.FloatTensor)`A `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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. The [FlavaForPreTraining](/docs/transformers/v5.7.0/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. - **loss** (`torch.FloatTensor`, *optional*, returned when `return_loss` is True) -- Total loss calculated for this model. - **loss_info** (`~models.flava.modeling_flava.FlavaLosses`, defaults to `None`) -- 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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageModel). - **image_output** (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present) -- The output of the [FlavaImageModel](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaTextModel). - **text_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present) -- The output of the [FlavaTextModel](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaTextModel). - **text_masked_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present) -- The output of the [FlavaTextModel](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaTextModel). - **multimodal_masked_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` and `pixel_values` are present) -- The output of the [FlavaMultimodalModel](/docs/transformers/v5.7.0/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. - **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. - **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. Examples: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import FlavaForPreTraining, AutoProcessor >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full") >>> processor = AutoProcessor.from_pretrained("facebook/flava-full") >>> text = ["a photo of a cat"] >>> inputs = processor( ... images=[image], ... text=text, ... return_masks=True, ... return_codebook_pixels=True, ... padding=True, ... max_length=77, ... return_tensors="pt", ... ) >>> output = model(**inputs) ``` **Parameters:** config ([FlavaConfig](/docs/transformers/v5.7.0/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()](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. image_codebook (`torch.nn.modules.module.Module`, *optional*) : 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. **Returns:** ``FlavaForPreTrainingOutput` or `tuple(torch.FloatTensor)`` A `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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. ## FlavaModel[[transformers.FlavaModel]] #### transformers.FlavaModel[[transformers.FlavaModel]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L944) The bare Flava Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also 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. forwardtransformers.FlavaModel.forwardhttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1087[{"name": "input_ids", "val": ": torch.LongTensor | None = None"}, {"name": "pixel_values", "val": ": torch.FloatTensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor | None = None"}, {"name": "bool_masked_pos", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.LongTensor | None = None"}, {"name": "image_attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "skip_multimodal_encoder", "val": ": bool | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool = True"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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](/docs/transformers/v5.7.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **pixel_values** (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor). See `FlavaImageProcessor.__call__()` for details ([FlavaProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaProcessor) uses [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor) for processing images). - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) - **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?](../glossary#token-type-ids) - **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). - **position_ids** (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **image_attention_mask** (`torch.Tensor` of shape `(batch_size, image_num_patches)`, *optional*) -- Mask to avoid performing attention on padding pixel values for image inputs. Mask values selected in `[0, 1]`: - 1 for pixel values that are real (i.e., **not masked**), - 0 for pixel values that are padding (i.e., **masked**). - **skip_multimodal_encoder** (`*bool*`, *optional*) -- Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used. - **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*, defaults to `True`) -- 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](/docs/transformers/v5.7.0/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0`FlavaModelOutput` or `tuple(torch.FloatTensor)`A `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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. The [FlavaModel](/docs/transformers/v5.7.0/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. - **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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageModel). - **image_output** (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present) -- The output of the [FlavaImageModel](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaTextModel). - **text_output** (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present) -- The output of the [FlavaTextModel](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/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](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaMultimodalModel). Examples: ```python >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> 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" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> 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]) ``` **Parameters:** config ([FlavaConfig](/docs/transformers/v5.7.0/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()](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. **Returns:** ``FlavaModelOutput` or `tuple(torch.FloatTensor)`` A `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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. #### get_text_features[[transformers.FlavaModel.get_text_features]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L990) - **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. Examples: ```python >>> import torch >>> from transformers import AutoProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = AutoProcessor.from_pretrained("{0}") >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> with torch.inference_mode(): ... text_features = model.get_text_features(**inputs) ``` **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](/docs/transformers/v5.7.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) 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?](../glossary#token-type-ids) position_ids (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) : Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. #### get_image_features[[transformers.FlavaModel.get_image_features]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1041) - **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. Examples: ```python >>> import torch >>> from transformers import AutoProcessor, FlavaModel >>> from transformers.image_utils import load_image >>> model = FlavaModel.from_pretrained("{0}") >>> processor = AutoProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = load_image(url) >>> inputs = processor(images=image, return_tensors="pt") >>> with torch.inference_mode(): ... image_features = model.get_image_features(**inputs) ``` **Parameters:** pixel_values (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`) : The tensors corresponding to the input images. Pixel values can be obtained using [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor). See `FlavaImageProcessor.__call__()` for details ([FlavaProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaProcessor) uses [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor) for processing images). 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.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. ## FlavaImageCodebook[[transformers.FlavaImageCodebook]] #### transformers.FlavaImageCodebook[[transformers.FlavaImageCodebook]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1298) 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 inherits from [PreTrainedModel](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also 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. forwardtransformers.FlavaImageCodebook.forwardhttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1383[{"name": "pixel_values", "val": ": FloatTensor"}, {"name": "**kwargs", "val": ""}] **Parameters:** config ([FlavaImageCodebookConfig](/docs/transformers/v5.7.0/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()](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. #### get_codebook_indices[[transformers.FlavaImageCodebook.get_codebook_indices]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1349) #### get_codebook_probs[[transformers.FlavaImageCodebook.get_codebook_probs]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L1379) ## FlavaTextModel[[transformers.FlavaTextModel]] #### transformers.FlavaTextModel[[transformers.FlavaTextModel]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L762) The bare Flava Text Model outputting raw hidden-states without any specific head on to. This model inherits from [PreTrainedModel](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also 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. forwardtransformers.FlavaTextModel.forwardhttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L790[{"name": "input_ids", "val": ": torch.Tensor | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "token_type_ids", "val": ": torch.Tensor | None = None"}, {"name": "position_ids", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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](/docs/transformers/v5.7.0/en/model_doc/auto#transformers.AutoTokenizer). See [PreTrainedTokenizer.encode()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode) and [PreTrainedTokenizer.__call__()](/docs/transformers/v5.7.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__) for details. [What are input IDs?](../glossary#input-ids) - **attention_mask** (`torch.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) - **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?](../glossary#token-type-ids) - **position_ids** (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) - **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](/docs/transformers/v5.7.0/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. The [FlavaTextModel](/docs/transformers/v5.7.0/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. - **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. **Parameters:** config ([FlavaTextConfig](/docs/transformers/v5.7.0/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()](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether to add a pooling layer **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. ## FlavaImageModel[[transformers.FlavaImageModel]] #### transformers.FlavaImageModel[[transformers.FlavaImageModel]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L680) The bare Flava Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also 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. forwardtransformers.FlavaImageModel.forwardhttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L710[{"name": "pixel_values", "val": ": torch.Tensor | None = None"}, {"name": "bool_masked_pos", "val": ": torch.BoolTensor | None = None"}, {"name": "interpolate_pos_encoding", "val": ": bool | None = None"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **pixel_values** (`torch.Tensor` of shape `(batch_size, num_channels, image_size, image_size)`, *optional*) -- The tensors corresponding to the input images. Pixel values can be obtained using [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor). See `FlavaImageProcessor.__call__()` for details ([FlavaProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaProcessor) uses [FlavaImageProcessor](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaImageProcessor) for processing images). - **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.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) - **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](/docs/transformers/v5.7.0/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. The [FlavaImageModel](/docs/transformers/v5.7.0/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. - **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. **Parameters:** config ([FlavaImageConfig](/docs/transformers/v5.7.0/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()](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether to add a pooling layer **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. ## FlavaMultimodalModel[[transformers.FlavaMultimodalModel]] #### transformers.FlavaMultimodalModel[[transformers.FlavaMultimodalModel]] [Source](https://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L862) The bare Flava Model outputting raw hidden-states without any specific head on top. This model inherits from [PreTrainedModel](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel). Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also 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. forwardtransformers.FlavaMultimodalModel.forwardhttps://github.com/huggingface/transformers/blob/v5.7.0/src/transformers/models/flava/modeling_flava.py#L886[{"name": "hidden_states", "val": ": Tensor"}, {"name": "attention_mask", "val": ": torch.Tensor | None = None"}, {"name": "output_attentions", "val": ": bool | None = None"}, {"name": "output_hidden_states", "val": ": bool | None = None"}, {"name": "return_dict", "val": ": bool | None = None"}, {"name": "**kwargs", "val": ""}]- **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.Tensor` of shape `(batch_size, sequence_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?](../glossary#attention-mask) - **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](/docs/transformers/v5.7.0/en/main_classes/output#transformers.utils.ModelOutput) instead of a plain tuple.0[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs. The [FlavaMultimodalModel](/docs/transformers/v5.7.0/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. - **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. **Parameters:** config ([FlavaMultimodalConfig](/docs/transformers/v5.7.0/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()](/docs/transformers/v5.7.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained) method to load the model weights. add_pooling_layer (`bool`, *optional*, defaults to `True`) : Whether to add a pooling layer **Returns:** `[BaseModelOutputWithPooling](/docs/transformers/v5.7.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPooling) or `tuple(torch.FloatTensor)`` A [BaseModelOutputWithPooling](/docs/transformers/v5.7.0/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 ([FlavaConfig](/docs/transformers/v5.7.0/en/model_doc/flava#transformers.FlavaConfig)) and inputs.