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 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ddgZy)zConvNeXT model configurationé    ©ÚOrderedDict)ÚMapping)Úversioné   )ÚPretrainedConfig)Ú
OnnxConfig)Úlogging)ÚBackboneConfigMixinÚ*get_aligned_output_features_output_indicesc                   óB   ‡ — e Zd ZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 dˆ fd„	Zˆ xZS )ÚConvNextConfigaî  
    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
    ```Úconvnextc                 ó¢  •— t        ‰| �  di |¤Ž || _        || _        || _        |€g d¢n|| _        |€g d¢n|| _        || _        || _        || _	        |	| _
        |
| _        || _        dgt        dt        | j                  «      dz   «      D �cg c]  }d|› �‘Œ	 c}z   | _        t!        ||| j                  ¬«      \  | _        | _        y c c}w )N)é`   éÀ   i€  i   )r   r   é	   r   Ústemé   Ústage)Úout_featuresÚout_indicesÚstage_names© )ÚsuperÚ__init__Únum_channelsÚ
patch_sizeÚ
num_stagesÚhidden_sizesÚdepthsÚ
hidden_actÚinitializer_rangeÚlayer_norm_epsÚlayer_scale_init_valueÚdrop_path_rateÚ
image_sizeÚrangeÚlenr   r   Ú_out_featuresÚ_out_indices)Úselfr   r   r   r    r!   r"   r#   r$   r%   r&   r'   r   r   ÚkwargsÚidxÚ	__class__s                   €úq/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/convnext/configuration_convnext.pyr   zConvNextConfig.__init__Z   sÔ   ø€ ô" 	‰ÑÑ"˜6Ò"à(ˆÔØ$ˆŒØ$ˆŒØ3?Ð3GÓ/È\ˆÔØ&, n“l¸&ˆŒØ$ˆŒØ!2ˆÔØ,ˆÔØ&<ˆÔ#Ø,ˆÔØ$ˆŒØ"˜8ÄÀaÌÈTÏ[É[ÓIYÐ\]ÑI]Ó@^Ö&_¸¨¨s¨e¢}Ò&_Ñ_ˆÔÜ0ZØ%°;ÈD×L\ÑL\ô1
Ñ-ˆÔ˜DÕ-ùò '`s   ÂC)r   é   r1   NNÚgelug{®Gáz”?gê-�™—q=g�íµ ÷Æ°>g        éà   NN)Ú__name__Ú
__module__Ú__qualname__Ú__doc__Ú
model_typer   Ú__classcell__)r/   s   @r0   r   r      sC   ø„ ñ6ðp €Jð ØØØØØØØØ#ØØØØ÷!
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edefd„«       Zy)ÚConvNextOnnxConfigz1.11Úreturnc                 ó(   — t        ddddddœfg«      S )NÚpixel_valuesÚbatchr   ÚheightÚwidth)r   r   é   r   r   ©r,   s    r0   ÚinputszConvNextOnnxConfig.inputs�   s&   € äà W°ÀHÐQXÑ!YÐZðó
ð 	
r:   c                  ó   — y)Ngñhãˆµøä>r   rD   s    r0   Úatol_for_validationz&ConvNextOnnxConfig.atol_for_validation‰   s   € àr:   N)r4   r5   r6   r   ÚparseÚtorch_onnx_minimum_versionÚpropertyr   ÚstrÚintrE   ÚfloatrG   r   r:   r0   r<   r<   ~   sZ   „ Ø!. §¡¨vÓ!6Ðàð
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ó ð
ð ð Uò ó ñr:   r<   N)r7   Úcollectionsr   Útypingr   Ú	packagingr   Úconfiguration_utilsr   Úonnxr	   Úutilsr
   Úutils.backbone_utilsr   r   Ú
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