import warnings """ Florence-2 configuration""" from typing import Optional from transformers import AutoConfig from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class Florence2VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Florence2VisionModel architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: drop_path_rate (`float`, *optional*, defaults to 0.1): The dropout rate of the drop path layer. patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]): The patch size of the image. patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]): The patch stride of the image. patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]): The patch padding of the image. patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]): Whether to apply layer normalization before the patch embedding layer. enable_checkpoint (`bool`, *optional*, defaults to False): Whether to enable checkpointing. dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]): The dimension of the embedding layer. num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]): The number of attention heads. num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]): The number of groups. depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]): The depth of the model. window_size (`int`, *optional*, defaults to 12): The window size of the model. projection_dim (`int`, *optional*, defaults to 1024): The dimension of the projection layer. visual_temporal_embedding (`dict`, *optional*): The configuration of the visual temporal embedding. image_pos_embed (`dict`, *optional*): The configuration of the image position embedding. image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]): The source of the image feature. Example: ```python >>> from transformers import Florence2VisionConfig, Florence2VisionModel >>> # Initializing a Florence2 Vision style configuration >>> configuration = Florence2VisionConfig() >>> # Initializing a model (with random weights) >>> model = Florence2VisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "florence2_vision" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, drop_path_rate=0.1, patch_size=[7, 3, 3, 3], patch_stride=[4, 2, 2, 2], patch_padding=[3, 1, 1, 1], patch_prenorm=[False, True, True, True], enable_checkpoint=False, dim_embed=[256, 512, 1024, 2048], num_heads=[8, 16, 32, 64], num_groups=[8, 16, 32, 64], depths=[1, 1, 9, 1], window_size=12, projection_dim=1024, visual_temporal_embedding=None, image_pos_embed=None, image_feature_source=["spatial_avg_pool", "temporal_avg_pool"], **kwargs, ): self.drop_path_rate = drop_path_rate self.patch_size = patch_size self.patch_stride = patch_stride self.patch_padding = patch_padding self.patch_prenorm = patch_prenorm self.enable_checkpoint = enable_checkpoint self.dim_embed = dim_embed self.num_heads = num_heads self.num_groups = num_groups self.depths = depths self.window_size = window_size self.projection_dim = projection_dim self.visual_temporal_embedding = visual_temporal_embedding self.image_pos_embed = image_pos_embed self.image_feature_source = image_feature_source super().__init__(**kwargs) class Florence2LanguageConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Florence2LanguagePreTrainedModel`]. It is used to instantiate a BART 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 BART [facebook/bart-large](https://huggingface.co/facebook/bart-large) 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 51289): Vocabulary size of the Florence2Language model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`Florence2LanguageModel`]. d_model (`int`, *optional*, defaults to 1024): Dimensionality of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`int`, *optional*, defaults to 1024): 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). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. scale_embedding (`bool`, *optional*, defaults to `False`): Scale embeddings by diving by sqrt(d_model). use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). num_labels (`int`, *optional*, defaults to 3): The number of labels to use in [`Florence2LanguageForSequenceClassification`]. forced_eos_token_id (`int`, *optional*, defaults to 2): The id of the token to force as the last generated token when `max_length` is reached. Usually set to `eos_token_id`. Example: ```python >>> from transformers import Florence2LanguageConfig, Florence2LanguageModel >>> # Initializing a Florence2 Language style configuration >>> configuration = Florence2LanguageConfig() >>> # Initializing a model (with random weights) >>> model = Florence2LangaugeModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "florence2_language" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=51289, max_position_embeddings=1024, encoder_layers=12, encoder_ffn_dim=4096, encoder_attention_heads=16, decoder_layers=12, decoder_ffn_dim=4096, decoder_attention_heads=16, encoder_layerdrop=0.0, decoder_layerdrop=0.0, activation_function="gelu", d_model=1024, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, use_cache=True, num_labels=3, pad_token_id=1, bos_token_id=0, eos_token_id=2, is_encoder_decoder=True, decoder_start_token_id=2, forced_eos_token_id=2, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True super().__init__( num_labels=num_labels, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=decoder_start_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) # ensure backward compatibility for BART CNN models if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): self.forced_bos_token_id = self.bos_token_id warnings.warn( f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " "The config can simply be saved and uploaded again to be fixed." ) class Florence2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Florence2ForConditionalGeneration`]. It is used to instantiate an Florence-2 model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vision_config (`Florence2VisionConfig`, *optional*): Custom vision config or dict text_config (`Union[AutoConfig, dict]`, *optional*): The config object of the text backbone. ignore_index (`int`, *optional*, defaults to -100): The ignore index for the loss function. vocab_size (`int`, *optional*, defaults to 51289): Vocabulary size of the Florence2model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`~Florence2ForConditionalGeneration`] projection_dim (`int`, *optional*, defaults to 1024): Dimension of the multimodal projection space. Example: ```python >>> from transformers import Florence2ForConditionalGeneration, Florence2Config, CLIPVisionConfig, BartConfig >>> # Initializing a clip-like vision config >>> vision_config = CLIPVisionConfig() >>> # Initializing a Bart config >>> text_config = BartConfig() >>> # Initializing a Florence-2 configuration >>> configuration = Florence2Config(vision_config, text_config) >>> # Initializing a model from the florence-2 configuration >>> model = Florence2ForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "florence2" is_composition = False def __init__( self, vision_config=None, text_config=None, ignore_index=-100, vocab_size=51289, projection_dim=1024, **kwargs, ): self.ignore_index = ignore_index self.vocab_size = vocab_size self.projection_dim = projection_dim if vision_config is not None: vision_config = PretrainedConfig(**vision_config) self.vision_config = vision_config self.vocab_size = self.vocab_size self.text_config = text_config if text_config is not None: self.text_config = Florence2LanguageConfig(**text_config) super().__init__(**kwargs)