o ZŽhnã@s@dZddlmZddlmZe e¡ZGdd„deƒZdgZ dS)zGLPN model configurationé)ÚPretrainedConfig)ÚloggingcsfeZdZdZdZddgd¢gd¢gd¢gd¢gd ¢gd ¢gd ¢d d d ddddddf‡fdd„ Z‡ZS)Ú GLPNConfiga? This is the configuration class to store the configuration of a [`GLPNModel`]. It is used to instantiate an GLPN 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 GLPN [vinvino02/glpn-kitti](https://huggingface.co/vinvino02/glpn-kitti) 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_channels (`int`, *optional*, defaults to 3): The number of input channels. num_encoder_blocks (`int`, *optional*, defaults to 4): The number of encoder blocks (i.e. stages in the Mix Transformer encoder). depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2]`): The number of layers in each encoder block. sr_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`): Sequence reduction ratios in each encoder block. hidden_sizes (`List[int]`, *optional*, defaults to `[32, 64, 160, 256]`): Dimension of each of the encoder blocks. patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3, 3]`): Patch size before each encoder block. strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`): Stride before each encoder block. num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`): Number of attention heads for each attention layer in each block of the Transformer encoder. mlp_ratios (`List[int]`, *optional*, defaults to `[4, 4, 4, 4]`): Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the encoder blocks. 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. drop_path_rate (`float`, *optional*, defaults to 0.1): The dropout probability for stochastic depth, used in the blocks of the Transformer encoder. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. decoder_hidden_size (`int`, *optional*, defaults to 64): The dimension of the decoder. max_depth (`int`, *optional*, defaults to 10): The maximum depth of the decoder. head_in_index (`int`, *optional*, defaults to -1): The index of the features to use in the head. Example: ```python >>> from transformers import GLPNModel, GLPNConfig >>> # Initializing a GLPN vinvino02/glpn-kitti style configuration >>> configuration = GLPNConfig() >>> # Initializing a model from the vinvino02/glpn-kitti style configuration >>> model = GLPNModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```Zglpnré)érrr)érré)é é@é é)érrr)rrrr)rrér)rrrrZgelugg{®Gáz”?gš™™™™™¹?gíµ ÷Æ°>r é éÿÿÿÿc s‚tƒjdi|¤Ž||_||_||_||_||_||_||_| |_ ||_ | |_ | |_ | |_ | |_||_||_||_||_||_dS)N©)ÚsuperÚ__init__Ú num_channelsÚnum_encoder_blocksÚdepthsÚ sr_ratiosÚ hidden_sizesÚ patch_sizesÚstridesÚ mlp_ratiosÚnum_attention_headsÚ hidden_actÚhidden_dropout_probÚattention_probs_dropout_probÚinitializer_rangeÚdrop_path_rateÚlayer_norm_epsÚdecoder_hidden_sizeÚ max_depthÚ head_in_index)Úselfrrrrrrrrrrrrr r!r"r#r$r%Úkwargs©Ú __class__rúZ/var/www/auris/lib/python3.10/site-packages/transformers/models/glpn/configuration_glpn.pyr[s& zGLPNConfig.__init__)Ú__name__Ú __module__Ú __qualname__Ú__doc__Z model_typerÚ __classcell__rrr(r*rs,@írN) r.Zconfiguration_utilsrÚutilsrZ get_loggerr+ÚloggerrÚ__all__rrrr*Ús     o