a h\@s@dZddlmZddlmZeeZGdddeZdgZ dS)zSegGpt model configuration)PretrainedConfig)loggingcsTeZdZdZdZddddddd d d gdd d ddddd dgddffdd ZZS) SegGptConfiga This is the configuration class to store the configuration of a [`SegGptModel`]. It is used to instantiate a SegGPT 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 SegGPT [BAAI/seggpt-vit-large](https://huggingface.co/BAAI/seggpt-vit-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: hidden_size (`int`, *optional*, defaults to 1024): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 24): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention 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. 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-06): The epsilon used by the layer normalization layers. image_size (`list[int]`, *optional*, defaults to `[896, 448]`): 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. mlp_dim (`int`, *optional*): The dimensionality of the MLP layer in the Transformer encoder. If unset, defaults to `hidden_size` * 4. drop_path_rate (`float`, *optional*, defaults to 0.1): The drop path rate for the dropout layers. pretrain_image_size (`int`, *optional*, defaults to 224): The pretrained size of the absolute position embeddings. decoder_hidden_size (`int`, *optional*, defaults to 64): Hidden size for decoder. use_relative_position_embeddings (`bool`, *optional*, defaults to `True`): Whether to use relative position embeddings in the attention layers. merge_index (`int`, *optional*, defaults to 2): The index of the encoder layer to merge the embeddings. intermediate_hidden_state_indices (`list[int]`, *optional*, defaults to `[5, 11, 17, 23]`): The indices of the encoder layers which we store as features for the decoder. beta (`float`, *optional*, defaults to 0.01): Regularization factor for SegGptLoss (smooth-l1 loss). Example: ```python >>> from transformers import SegGptConfig, SegGptModel >>> # Initializing a SegGPT seggpt-vit-large style configuration >>> configuration = SegGptConfig() >>> # Initializing a model (with random weights) from the seggpt-vit-large style configuration >>> model = SegGptModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ZseggptiZgelugg{Gz?gư>iirTNg?@) g{Gz?c stjfi||t|kr2td|d|||_||_||_||_||_||_ ||_ ||_ | |_ | |_ | |_| |_||_||_||_||_||_||_| durt|dn| |_dS)NzTMerge index must be less than the minimum encoder output index, but got merge_index=z' and intermediate_hidden_state_indices=)super__init__min ValueError hidden_sizenum_hidden_layersnum_attention_heads hidden_acthidden_dropout_probinitializer_rangelayer_norm_eps image_size patch_size num_channelsqkv_biasdrop_path_ratepretrain_image_sizedecoder_hidden_size use_relative_position_embeddings merge_index!intermediate_hidden_state_indicesbetaintmlp_dim)selfrrrrrrrrrrrr&rrr r!r"r#r$kwargs __class__k/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/seggpt/configuration_seggpt.pyr]s0 zSegGptConfig.__init__)__name__ __module__ __qualname____doc__Z model_typer __classcell__r+r+r)r,rs,BrN) r0Zconfiguration_utilsrutilsrZ get_loggerr-loggerr__all__r+r+r+r,s    w