S^(h`dZddlmZddlmZej eZGddeZdgZ y)zCvT model configuration)PretrainedConfig)loggingceZdZdZdZdgdgdgdgdgdgd gd gd gd gd gd gdgdgdgdgdgdgdddffd ZxZS) CvtConfigaO This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT 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 CvT [microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) 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. patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3]`): The kernel size of each encoder's patch embedding. patch_stride (`List[int]`, *optional*, defaults to `[4, 2, 2]`): The stride size of each encoder's patch embedding. patch_padding (`List[int]`, *optional*, defaults to `[2, 1, 1]`): The padding size of each encoder's patch embedding. embed_dim (`List[int]`, *optional*, defaults to `[64, 192, 384]`): Dimension of each of the encoder blocks. num_heads (`List[int]`, *optional*, defaults to `[1, 3, 6]`): Number of attention heads for each attention layer in each block of the Transformer encoder. depth (`List[int]`, *optional*, defaults to `[1, 2, 10]`): The number of layers in each encoder block. mlp_ratios (`List[float]`, *optional*, defaults to `[4.0, 4.0, 4.0, 4.0]`): Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the encoder blocks. attention_drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`): The dropout ratio for the attention probabilities. drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`): The dropout ratio for the patch embeddings probabilities. drop_path_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.1]`): The dropout probability for stochastic depth, used in the blocks of the Transformer encoder. qkv_bias (`List[bool]`, *optional*, defaults to `[True, True, True]`): The bias bool for query, key and value in attentions cls_token (`List[bool]`, *optional*, defaults to `[False, False, True]`): Whether or not to add a classification token to the output of each of the last 3 stages. qkv_projection_method (`List[string]`, *optional*, defaults to ["dw_bn", "dw_bn", "dw_bn"]`): The projection method for query, key and value Default is depth-wise convolutions with batch norm. For Linear projection use "avg". kernel_qkv (`List[int]`, *optional*, defaults to `[3, 3, 3]`): The kernel size for query, key and value in attention layer padding_kv (`List[int]`, *optional*, defaults to `[1, 1, 1]`): The padding size for key and value in attention layer stride_kv (`List[int]`, *optional*, defaults to `[2, 2, 2]`): The stride size for key and value in attention layer padding_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`): The padding size for query in attention layer stride_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`): The stride size for query in attention layer 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-6): The epsilon used by the layer normalization layers. Example: ```python >>> from transformers import CvtConfig, CvtModel >>> # Initializing a Cvt msft/cvt style configuration >>> configuration = CvtConfig() >>> # Initializing a model (with random weights) from the msft/cvt style configuration >>> model = CvtModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```cvtr)rr)r )r r )@i)r r)r r )@rr)rr)rrg?)TTT)FFT)dw_bnrr)rrr)r r r )r r r g{Gz?g-q=c Jt|di|||_||_||_||_||_||_||_||_ | |_ | |_ | |_ | |_ | |_||_||_||_||_||_||_||_||_y)N)super__init__ num_channels patch_sizes patch_stride patch_padding embed_dim num_headsdepth mlp_ratioattention_drop_rate drop_ratedrop_path_rateqkv_bias cls_tokenqkv_projection_method kernel_qkv padding_kv stride_kv padding_qstride_qinitializer_rangelayer_norm_eps)selfrrrrrrrrrr r!r"r#r$r%r&r'r(r)r*r+kwargs __class__s g/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/cvt/configuration_cvt.pyrzCvtConfig.__init__as2 "6"(&(*"" "#6 ",  "%:"$$""  !2,)__name__ __module__ __qualname____doc__ model_typer __classcell__)r.s@r/rrs\D LJ !+!&#&9-.-.-r0rN) r4configuration_utilsrutilsr get_loggerr1loggerr__all__rr0r/r<s=3   H %w- w-t -r0