
    %	&hD                     r    d Z ddlmZ ddlmZ ddlmZmZ  ej                  e	      Z
 G d dee      ZdgZy)zTextNet model configuration    )PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc                   J     e Zd ZdZdZdddddddgddg d	d
dddf fd	Z xZS )TextNetConfiga6  
    This is the configuration class to store the configuration of a [`TextNextModel`]. It is used to instantiate a
    TextNext 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
    [czczup/textnet-base](https://huggingface.co/czczup/textnet-base). Configuration objects inherit from
    [`PretrainedConfig`] and can be used to control the model outputs.Read the documentation from [`PretrainedConfig`]
    for more information.

    Args:
        stem_kernel_size (`int`, *optional*, defaults to 3):
            The kernel size for the initial convolution layer.
        stem_stride (`int`, *optional*, defaults to 2):
            The stride for the initial convolution layer.
        stem_num_channels (`int`, *optional*, defaults to 3):
            The num of channels in input for the initial convolution layer.
        stem_out_channels (`int`, *optional*, defaults to 64):
            The num of channels in out for the initial convolution layer.
        stem_act_func (`str`, *optional*, defaults to `"relu"`):
            The activation function for the initial convolution layer.
        image_size (`Tuple[int, int]`, *optional*, defaults to `[640, 640]`):
            The size (resolution) of each image.
        conv_layer_kernel_sizes (`List[List[List[int]]]`, *optional*):
            A list of stage-wise kernel sizes. If `None`, defaults to:
            `[[[3, 3], [3, 3], [3, 3]], [[3, 3], [1, 3], [3, 3], [3, 1]], [[3, 3], [3, 3], [3, 1], [1, 3]], [[3, 3], [3, 1], [1, 3], [3, 3]]]`.
        conv_layer_strides (`List[List[int]]`, *optional*):
            A list of stage-wise strides. If `None`, defaults to:
            `[[1, 2, 1], [2, 1, 1, 1], [2, 1, 1, 1], [2, 1, 1, 1]]`.
        hidden_sizes (`List[int]`, *optional*, defaults to `[64, 64, 128, 256, 512]`):
            Dimensionality (hidden size) at each stage.
        batch_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the batch normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        out_features (`List[str]`, *optional*):
            If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
            (depending on how many stages the model has). If unset and `out_indices` is set, will default to the
            corresponding stages. If unset and `out_indices` is unset, will default to the last stage.
        out_indices (`List[int]`, *optional*):
            If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
            many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
            If unset and `out_features` is unset, will default to the last stage.

    Examples:

    ```python
    >>> from transformers import TextNetConfig, TextNetBackbone

    >>> # Initializing a TextNetConfig
    >>> configuration = TextNetConfig()

    >>> # Initializing a model (with random weights)
    >>> model = TextNetBackbone(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```textnet      @   relui  N)r   r         i   gh㈵>g{Gz?c                 B   t        |   d	i | |3ddgddgddggddgddgddgddggddgddgddgddggddgddgddgddggg}|g dg dg dg dg}|| _        || _        || _        || _        || _        || _        || _        || _	        || _
        |	| _        |
| _        | j                  D cg c]  }t        |       c}| _        dgt        dd      D cg c]  }d| 	 c}z   | _        t#        ||| j                         \  | _        | _        y c c}w c c}w )
Nr
      )r   r   r   )r   r   r   r   stem   stage)out_featuresout_indicesstage_names )super__init__stem_kernel_sizestem_stridestem_num_channelsstem_out_channelsstem_act_func
image_sizeconv_layer_kernel_sizesconv_layer_stridesinitializer_rangehidden_sizesbatch_norm_epslendepthsranger   r   _out_features_out_indices)selfr   r   r   r   r   r    r!   r"   r$   r%   r#   r   r   kwargslayeridx	__class__s                    /var/www/pru.catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/textnet/configuration_textnet.pyr   zTextNetConfig.__init__U   sh   " 	"6""*Q!Q!Q(Q!Q!Q!Q0Q!Q!Q!Q0Q!Q!Q!Q0	'# %"+\<!V 0&!2!2*$'>$"4!2(,/3/K/KLes5zL"8a&Lse}&LL0Z%;DL\L\1
-D- M&Ls   1DD)__name__
__module____qualname____doc__
model_typer   __classcell__)r/   s   @r0   r   r      sG    7r J : $,/
 /
    r   N)r4   transformersr   transformers.utilsr   !transformers.utils.backbone_utilsr   r   
get_loggerr1   loggerr   __all__r   r7   r0   <module>r>      sD    " ) & m 
		H	%k
')9 k
\ 
r7   