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GLPNConfiga¦  
    num_encoder_blocks (`int`, *optional*, defaults to 4):
        The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
    depths (`list[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
        The number of layers in each encoder block.
    sr_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
        Sequence reduction ratios in each encoder block.
    patch_sizes (`list[int]`, *optional*, defaults to `[7, 3, 3, 3]`):
        Patch size before each encoder block.
    strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
        Stride before each encoder block.
    num_attention_heads (`list[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
        Number of attention heads for each attention layer in each block of the Transformer encoder.
    mlp_ratios (`list[int]`, *optional*, defaults to `[4, 4, 4, 4]`):
        Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
        encoder blocks.
    decoder_hidden_size (`int`, *optional*, defaults to 64):
        The dimension of the decoder.
    max_depth (`int`, *optional*, defaults to 10):
        The maximum depth of the decoder.
    head_in_index (`int`, *optional*, defaults to -1):
        The index of the features to use in the head.

    Example:

    ```python
    >>> from transformers import GLPNModel, GLPNConfig

    >>> # Initializing a GLPN vinvino02/glpn-kitti style configuration
    >>> configuration = GLPNConfig()

    >>> # Initializing a model from the vinvino02/glpn-kitti style configuration
    >>> model = GLPNModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Úglpnr   Únum_channelsé   Únum_encoder_blocks)é   r   r   r   .Údepths)é   r   r   é   Ú	sr_ratios)é    é@   é    é   Úhidden_sizes)é   r   r   r   Úpatch_sizes)r   r   r   r   Ústrides)r   r   é   r   Únum_attention_heads)r   r   r   r   Ú
mlp_ratiosÚgeluÚ
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   Ú	max_depthéÿÿÿÿÚhead_in_indexN)Ú__name__Ú
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