
    sg                         d Z ddlmZ ddlmZ ddlmZ ddlm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 G d de
      Zy)zConvNeXT model configuration    OrderedDict)Mapping)version   )PretrainedConfig)
OnnxConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc                   B     e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )ConvNextConfiga  
    This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
    ConvNeXT 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 ConvNeXT
    [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) 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_size (`int`, *optional*, defaults to 4):
            Patch size to use in the patch embedding layer.
        num_stages (`int`, *optional*, defaults to 4):
            The number of stages in the model.
        hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):
            Dimensionality (hidden size) at each stage.
        depths (`List[int]`, *optional*, defaults to [3, 3, 9, 3]):
            Depth (number of blocks) for each stage.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
            `"selu"` and `"gelu_new"` are supported.
        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-12):
            The epsilon used by the layer normalization layers.
        layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
            The initial value for the layer scale.
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            The drop rate for stochastic depth.
        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. Must be in the
            same order as defined in the `stage_names` attribute.
        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. Must be in the
            same order as defined in the `stage_names` attribute.

    Example:
    ```python
    >>> from transformers import ConvNextConfig, ConvNextModel

    >>> # Initializing a ConvNext convnext-tiny-224 style configuration
    >>> configuration = ConvNextConfig()

    >>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
    >>> model = ConvNextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```convnextc                    t        |   di | || _        || _        || _        |g dn|| _        |g dn|| _        || _        || _        || _	        |	| _
        |
| _        || _        dgt        dt        | j                        dz         D cg c]  }d| 	 c}z   | _        t!        ||| j                        \  | _        | _        y c c}w )N)`      i  i   )r   r   	   r   stem   stage)out_featuresout_indicesstage_names )super__init__num_channels
patch_size
num_stageshidden_sizesdepths
hidden_actinitializer_rangelayer_norm_epslayer_scale_init_valuedrop_path_rate
image_sizerangelenr   r   _out_features_out_indices)selfr   r   r   r    r!   r"   r#   r$   r%   r&   r'   r   r   kwargsidx	__class__s                   f/var/www/html/venv/lib/python3.12/site-packages/transformers/models/convnext/configuration_convnext.pyr   zConvNextConfig.__init__Z   s    " 	"6"($$3?3G/\&,nl&$!2,&<#,$"8aT[[IY\]I]@^&_se}&__0Z%;DL\L\1
-D- '`s   C)r      r1   NNgelug{Gz?g-q=gư>g           NN)__name__
__module____qualname____doc__
model_typer   __classcell__)r/   s   @r0   r   r      sC    6p J #!
 !
    r   c                   p    e Zd Z ej                  d      Zedeeee	ef   f   fd       Z
edefd       Zy)ConvNextOnnxConfigz1.11returnc                 (    t        ddddddfg      S )Npixel_valuesbatchr   heightwidth)r   r      r   r   r,   s    r0   inputszConvNextOnnxConfig.inputs   s&    WHQX!YZ
 	
r:   c                      y)Ngh㈵>r   rD   s    r0   atol_for_validationz&ConvNextOnnxConfig.atol_for_validation   s    r:   N)r4   r5   r6   r   parsetorch_onnx_minimum_versionpropertyr   strintrE   floatrG   r   r:   r0   r<   r<   ~   sZ    !.v!6
WS#X%6 67 
 
 U  r:   r<   N)r7   collectionsr   typingr   	packagingr   configuration_utilsr   onnxr	   utilsr
   utils.backbone_utilsr   r   
get_loggerr4   loggerr   r<   r   r:   r0   <module>rW      sR    # #   3   c 
		H	%\
(*: \
~ r:   