o
    ZhD                     @   sR   d Z ddlmZ ddlmZ ddlmZmZ ee	Z
G dd deeZdgZdS )zTextNet model configuration    )PretrainedConfig)logging)BackboneConfigMixin*get_aligned_output_features_output_indicesc                       sH   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d	Z  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
    ```Ztextnet      @   Zrelui  N)r	   r	         i   gh㈵>g{Gz?c                    s0  t  jdi | |d u r@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ddgddgddgddggg}|d u rRg dg dg dg dg}|| _|| _|| _|| _|| _|| _|| _|| _	|| _
|	| _|
| _dd | jD | _dgdd tdd	D  | _t||| jd
\| _| _d S )Nr      )r   r   r   )r   r   r   r   c                 S   s   g | ]}t |qS  )len).0layerr   r   `/var/www/auris/lib/python3.10/site-packages/transformers/models/textnet/configuration_textnet.py
<listcomp>   s    z*TextNetConfig.__init__.<locals>.<listcomp>stemc                 S   s   g | ]}d | qS )Zstager   )r   idxr   r   r   r      s       )out_featuresout_indicesstage_namesr   )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_epsZdepthsranger   r   Z_out_featuresZ_out_indices)selfr   r   r   r   r   r    r!   r"   r$   r%   r#   r   r   kwargs	__class__r   r   r   U   s2   zTextNetConfig.__init__)__name__
__module____qualname____doc__Z
model_typer   __classcell__r   r   r)   r   r      s"    9r   N)r.   Ztransformersr   Ztransformers.utilsr   Z!transformers.utils.backbone_utilsr   r   Z
get_loggerr+   loggerr   __all__r   r   r   r   <module>   s   

n