# coding=utf-8 # Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """FLAVA model configurations""" from typing import Any, Dict from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class FlavaImageConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FlavaImageModel`]. It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. mask_token (`bool`, *optional*, defaults to `True`): Whether to use a mask token or not. Used in MIM (Masked Image Modeling) loss for FLAVA. vocab_size (`int`, *optional*, defaults to 8192): Vocabulary size of the [`FlavaImageCodebook`] used in conjunction with [`FlavaImageModel`] for MIM (Masked Image Modeling) loss for FLAVA. 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 ```""" model_type = "flava_image_model" base_config_key = "image_config" def __init__( self, hidden_size: int = 768, num_hidden_layers: int = 12, num_attention_heads: int = 12, intermediate_size: int = 3072, hidden_act: int = "gelu", hidden_dropout_prob: float = 0.0, attention_probs_dropout_prob: float = 0.0, initializer_range: float = 0.02, layer_norm_eps: float = 1e-12, image_size: int = 224, patch_size: int = 16, num_channels: int = 3, qkv_bias: bool = True, mask_token: bool = True, vocab_size: int = 8192, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias self.mask_token = mask_token self.vocab_size = vocab_size class FlavaTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FlavaTextModel`]. It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 30522): Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FlavaTextModel`]. type_vocab_size (`int`, *optional*, defaults to 2): The vocabulary size of the `token_type_ids` passed when calling [`FlavaTextModel`]. Note that even though text encoder allows `token_type_ids`'s value as 2, for text-only pretraining and fine-tuning, only 1 is used similar to RoBERTa. max_position_embeddings (`int`, *optional*, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). For VL, max_length passed to model is 77. position_embedding_type (`str`, *optional*, defaults to `"absolute"`): Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. 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 ```""" model_type = "flava_text_model" base_config_key = "text_config" def __init__( self, vocab_size: int = 30522, type_vocab_size: int = 2, max_position_embeddings: int = 512, position_embedding_type: str = "absolute", hidden_size: int = 768, num_hidden_layers: int = 12, num_attention_heads: int = 12, intermediate_size: int = 3072, hidden_act: str = "gelu", hidden_dropout_prob: float = 0.0, attention_probs_dropout_prob: float = 0.0, initializer_range: float = 0.02, layer_norm_eps: float = 1e-12, pad_token_id: int = 0, qkv_bias: bool = True, **kwargs, ): super().__init__(**kwargs) self.vocab_size = vocab_size self.type_vocab_size = type_vocab_size self.max_position_embeddings = max_position_embeddings self.position_embedding_type = position_embedding_type self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.pad_token_id = pad_token_id class FlavaMultimodalConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`FlavaMultimodalModel`]. It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. use_cls_token (`bool`, *optional*, defaults to `True`): Whether to use an extra CLS token for multimodal settings. Usually needed by the FLAVA model. 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 ```""" model_type = "flava_multimodal_model" base_config_key = "multimodal_config" def __init__( self, hidden_size: int = 768, num_hidden_layers: int = 6, num_attention_heads: int = 12, intermediate_size: int = 3072, hidden_act: int = "gelu", hidden_dropout_prob: int = 0.0, attention_probs_dropout_prob: int = 0.0, initializer_range: float = 0.02, layer_norm_eps: float = 1e-12, qkv_bias: bool = True, use_cls_token: bool = True, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.use_cls_token = use_cls_token class FlavaImageCodebookConfig(PretrainedConfig): model_type = "flava_image_codebook" base_config_key = "image_codebook_config" r""" [`FlavaImageCodebookConfig`] is the configuration class to store the configuration of a [`FlavaImageCodebook`]. It is used to instantiate an FLAVA model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-image-codebook](https://huggingface.co/facebook/flava-image-codebook) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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): Number of channels in the image to be passed. num_blocks_per_group (`int`, *optional*, defaults to 2): Number of conv-based blocks per group. hidden_size (`int`, *optional*, defaults to 256): Size of hidden dim for the blocks. vocab_size (`int`, *optional*, defaults to 8192): Size of the output vocabulary for the codebook. 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. kwargs (*optional*): Dictionary of keyword arguments. 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 ``` """ def __init__( self, num_groups: int = 4, input_channels: int = 3, num_blocks_per_group: int = 2, hidden_size: int = 256, vocab_size: int = 8192, freeze: int = True, initializer_range: float = 0.02, **kwargs, ): super().__init__(**kwargs) self.num_groups = num_groups self.input_channels = input_channels self.num_blocks_per_group = num_blocks_per_group self.hidden_size = hidden_size self.vocab_size = vocab_size self.freeze = freeze self.initializer_range = initializer_range class FlavaConfig(PretrainedConfig): r""" [`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook and multimodal model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`FlavaTextConfig`]. image_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`FlavaImageConfig`]. multimodal_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`FlavaMultimodalConfig`]. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. projection_dim (`int`, *optional*, defaults to 512): Dimensionality of text and image projection layers. logit_scale_init_value (`float`, *optional*, defaults to 2.6592): The initial value of the *logit_scale* parameter. Default is used as per the original FLAVA/CLIP implementation. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. ce_ignore_index (`int`, *optional*, defaults to -100): Cross entropy index to ignore. mim_weight (`float`, *optional*, defaults to 1.0): Weight to be assigned to MIM (Masked Image Modeling) unimodal loss mlm_weight (`float`, *optional*, defaults to 1.0): Weight to be assigned to MLM (Masked Language Modeling) unimodal loss global_contrastive_weight (`float`, *optional*, defaults to 1.0): Weight to be assigned to global contrastive cross-alignment loss. itm_weight (`float`, *optional*, defaults to 1.0): Weight to be assigned to image-text matching multimodal loss. mmm_image_weight (`float`, *optional*, defaults to 1.0): Weight to be assigned to MMM loss's image part. mmm_text_weight (`float`, *optional*, defaults to 1.0): Weight to be assigned to MMM loss's text part. global_backprop_contrastive (`bool`, *optional*, defaults to `True`): Whether to use global backpropgation through all workers in contrastive loss. skip_unmasked_multimodal_encoder (`bool`, *optional*, defaults to `True`): Whether to skip running unmasked multimodal encoder whose outputs are not used by FLAVA losses. return_loss (`bool`, *optional*, defaults to `True`): Whether to return loss or not kwargs (*optional*): Dictionary of keyword arguments. 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 ``` """ model_type = "flava" sub_configs = { "text_config": FlavaTextConfig, "image_config": FlavaImageConfig, "multimodal_config": FlavaMultimodalConfig, "image_codebook_config": FlavaImageCodebookConfig, } def __init__( self, image_config: Dict[str, Any] = None, text_config: Dict[str, Any] = None, multimodal_config: Dict[str, Any] = None, image_codebook_config: Dict[str, Any] = None, hidden_size: int = 768, layer_norm_eps: float = 1e-12, projection_dim: int = 768, init_codebook: bool = True, logit_scale_init_value: float = 2.6592, initializer_range: float = 0.02, ce_ignore_index: int = -100, mim_weight: float = 1.0, mlm_weight: float = 1.0, global_contrastive_weight: float = 1.0, itm_weight: float = 1.0, mmm_image_weight: float = 1.0, mmm_text_weight: float = 1.0, global_backprop_contrastive: bool = True, skip_unmasked_multimodal_encoder: bool = True, return_loss: bool = True, **kwargs, ): # If `_config_dict` exist, we use them for the backward compatibility. # We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot # of confusion!). text_config_dict = kwargs.pop("text_config_dict", None) image_config_dict = kwargs.pop("image_config_dict", None) multimodal_config_dict = kwargs.pop("multimodal_config_dict", None) image_codebook_config_dict = kwargs.pop("image_codebook_config_dict", None) super().__init__(**kwargs) # Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in # `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most # cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`. if text_config_dict is not None: if text_config is None: text_config = {} # This is the complete result when using `text_config_dict`. _text_config_dict = FlavaTextConfig(**text_config_dict).to_dict() # Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different. for key, value in _text_config_dict.items(): if key in text_config and value != text_config[key] and key not in ["transformers_version"]: # If specified in `text_config_dict` if key in text_config_dict: message = ( f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. " f'The value `text_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`text_config_dict` is provided which will be used to initialize `FlavaTextConfig`. The " f'value `text_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `text_config` with the ones in `_text_config_dict`. text_config.update(_text_config_dict) if image_config_dict is not None: if image_config is None: image_config = {} # This is the complete result when using `image_config_dict`. _image_config_dict = FlavaImageConfig(**image_config_dict).to_dict() # convert keys to string instead of integer if "id2label" in _image_config_dict: _image_config_dict["id2label"] = { str(key): value for key, value in _image_config_dict["id2label"].items() } # Give a warning if the values exist in both `_image_config_dict` and `image_config` but being different. for key, value in _image_config_dict.items(): if key in image_config and value != image_config[key] and key not in ["transformers_version"]: # If specified in `image_config_dict` if key in image_config_dict: message = ( f"`{key}` is found in both `image_config_dict` and `image_config` but with different " f'values. The value `image_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`image_config_dict` is provided which will be used to initialize `FlavaImageConfig`. " f'The value `image_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `image_config` with the ones in `_image_config_dict`. image_config.update(_image_config_dict) if multimodal_config_dict is not None: if multimodal_config is None: multimodal_config = {} # This is the complete result when using `multimodal_config_dict`. _multimodal_config_dict = FlavaMultimodalConfig(**multimodal_config_dict).to_dict() # Give a warning if the values exist in both `_multimodal_config_dict` and `multimodal_config` but being # different. for key, value in _multimodal_config_dict.items(): if ( key in multimodal_config and value != multimodal_config[key] and key not in ["transformers_version"] ): # If specified in `multimodal_config_dict` if key in multimodal_config_dict: message = ( f"`{key}` is found in both `multimodal_config_dict` and `multimodal_config` but with " f'different values. The value `multimodal_config_dict["{key}"]` will be used instead.' ) # If inferred from default argument values (just to be super careful) else: message = ( f"`multimodal_config_dict` is provided which will be used to initialize " f'`FlavaMultimodalConfig`. The value `multimodal_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `multimodal_config` with the ones in `_multimodal_config_dict`. multimodal_config.update(_multimodal_config_dict) if image_codebook_config_dict is not None: if image_codebook_config is None: image_codebook_config = {} # This is the complete result when using `image_codebook_config_dict`. _image_codebook_config_dict = FlavaImageCodebookConfig(**image_codebook_config_dict).to_dict() # Give a warning if the values exist in both `_image_codebook_config_dict` and `image_codebook_config` but # being different. for key, value in _image_codebook_config_dict.items(): if ( key in image_codebook_config and value != image_codebook_config[key] and key not in ["transformers_version"] ): # If specified in `image_codebook_config_dict` if key in image_codebook_config_dict: message = ( f"`{key}` is found in both `image_codebook_config_dict` and `image_codebook_config` but " f'with different values. The value `image_codebook_config_dict["{key}"]` will be used ' "instead." ) # If inferred from default argument values (just to be super careful) else: message = ( f"`image_codebook_config_dict` is provided which will be used to initialize " f'`FlavaImageCodebookConfig`. The value `image_codebook_config["{key}"]` will be overridden.' ) logger.info(message) # Update all values in `image_codebook_config` with the ones in `_image_codebook_config_dict`. image_codebook_config.update(_image_codebook_config_dict) if image_config is None: image_config = {} logger.info("`image_config` is `None`. initializing the `FlavaImageConfig` with default values.") if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `FlavaTextConfig` with default values.") if multimodal_config is None: multimodal_config = {} logger.info("`multimodal_config` is `None`. initializing the `FlavaMultimodalConfig` with default values.") if image_codebook_config is None: image_codebook_config = {} logger.info( "`image_codebook_config` is `None`. initializing the `FlavaImageCodebookConfig` with default values." ) self.image_config = FlavaImageConfig(**image_config) self.text_config = FlavaTextConfig(**text_config) self.multimodal_config = FlavaMultimodalConfig(**multimodal_config) self.image_codebook_config = FlavaImageCodebookConfig(**image_codebook_config) self.projection_dim = projection_dim self.init_codebook = init_codebook self.hidden_size = hidden_size self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.logit_scale_init_value = logit_scale_init_value self.initializer_factor = 1.0 self.ce_ignore_index = ce_ignore_index self.mim_weight = mim_weight self.mlm_weight = mlm_weight self.global_contrastive_weight = global_contrastive_weight self.itm_weight = itm_weight self.mmm_image_weight = mmm_image_weight self.mmm_text_weight = mmm_text_weight self.global_backprop_contrastive = global_backprop_contrastive self.skip_unmasked_multimodal_encoder = skip_unmasked_multimodal_encoder self.return_loss = return_loss @classmethod def from_configs( cls, image_config: FlavaImageConfig, text_config: FlavaTextConfig, multimodal_config: FlavaMultimodalConfig, image_codebook_config: FlavaImageCodebookConfig, **kwargs, ): r""" Instantiate a [`FlavaConfig`] (or a derived class) from flava text model configuration, flava image model configuration, flava multimodal model and flava codebook model configuration. Returns: [`FlavaConfig`]: An instance of a configuration object """ return cls( image_config=image_config.to_dict(), text_config=text_config.to_dict(), multimodal_config=multimodal_config.to_dict(), image_codebook_config=image_codebook_config.to_dict(), **kwargs, ) __all__ = ["FlavaConfig", "FlavaImageCodebookConfig", "FlavaImageConfig", "FlavaMultimodalConfig", "FlavaTextConfig"]